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JDK 17 jdk.incubator.vector.jmod - JDK Incubator Vector
JDK 17 jdk.incubator.vector.jmod is the JMOD file for JDK 17 HTTP Server module.
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⏎ jdk/incubator/vector/Vector.java
/* * Copyright (c) 2017, 2020, Oracle and/or its affiliates. All rights reserved. * ORACLE PROPRIETARY/CONFIDENTIAL. Use is subject to license terms. * * * * * * * * * * * * * * * * * * * * */ package jdk.incubator.vector; import java.nio.ByteBuffer; import java.nio.ByteOrder; import java.util.Arrays; /** * A * * <!-- The following paragraphs are shared verbatim * -- between Vector.java and package-info.java --> * sequence of a fixed number of <em>lanes</em>, * all of some fixed * {@linkplain Vector#elementType() <em>element type</em>} * such as {@code byte}, {@code long}, or {@code float}. * Each lane contains an independent value of the element type. * Operations on vectors are typically * <a href="Vector.html#lane-wise"><em>lane-wise</em></a>, * distributing some scalar operator (such as * {@linkplain Vector#add(Vector) addition}) * across the lanes of the participating vectors, * usually generating a vector result whose lanes contain the various * scalar results. When run on a supporting platform, lane-wise * operations can be executed in parallel by the hardware. This style * of parallelism is called <em>Single Instruction Multiple Data</em> * (SIMD) parallelism. * * <p> In the SIMD style of programming, most of the operations within * a vector lane are unconditional, but the effect of conditional * execution may be achieved using * <a href="Vector.html#masking"><em>masked operations</em></a> * such as {@link Vector#blend(Vector,VectorMask) blend()}, * under the control of an associated {@link VectorMask}. * Data motion other than strictly lane-wise flow is achieved using * <a href="Vector.html#cross-lane"><em>cross-lane</em></a> * operations, often under the control of an associated * {@link VectorShuffle}. * Lane data and/or whole vectors can be reformatted using various * kinds of lane-wise * {@linkplain Vector#convert(VectorOperators.Conversion,int) conversions}, * and byte-wise reformatting * {@linkplain Vector#reinterpretShape(VectorSpecies,int) reinterpretations}, * often under the control of a reflective {@link VectorSpecies} * object which selects an alternative vector format different * from that of the input vector. * * <p> {@code Vector<E>} declares a set of vector operations (methods) * that are common to all element types. These common operations * include generic access to lane values, data selection and movement, * reformatting, and certain arithmetic and logical operations (such as addition * or comparison) that are common to all primitive types. * * <p> <a href="Vector.html#subtypes">Public subtypes of {@code Vector}</a> * correspond to specific * element types. These declare further operations that are specific * to that element type, including unboxed access to lane values, * bitwise operations on values of integral element types, or * transcendental operations on values of floating point element * types. * * <p> Some lane-wise operations, such as the {@code add} operator, are defined as * a full-service named operation, where a corresponding method on {@code Vector} * comes in masked and unmasked overloadings, and (in subclasses) also comes in * covariant overrides (returning the subclass) and additional scalar-broadcast * overloadings (both masked and unmasked). * * Other lane-wise operations, such as the {@code min} operator, are defined as a * partially serviced (not a full-service) named operation, where a corresponding * method on {@code Vector} and/or a subclass provide some but all possible * overloadings and overrides (commonly the unmasked varient with scalar-broadcast * overloadings). * * Finally, all lane-wise operations (those named as previously described, * or otherwise unnamed method-wise) have a corresponding * {@link VectorOperators.Operator operator token} * declared as a static constant on {@link VectorOperators}. * Each operator token defines a symbolic Java expression for the operation, * such as {@code a + b} for the * {@link VectorOperators#ADD ADD} operator token. * General lane-wise operation-token accepting methods, such as for a * {@linkplain Vector#lanewise(VectorOperators.Unary) unary lane-wise} * operation, are provided on {@code Vector} and come in the same variants as * a full-service named operation. * * <p>This package contains a public subtype of {@link Vector} * corresponding to each supported element type: * {@link ByteVector}, {@link ShortVector}, * {@link IntVector}, {@link LongVector}, * {@link FloatVector}, and {@link DoubleVector}. * * <!-- The preceding paragraphs are shared verbatim * -- between Vector.java and package-info.java --> * * <p><a id="ETYPE"></a> The {@linkplain #elementType element type} of a vector, * referred to as {@code ETYPE}, is one of the primitive types * {@code byte}, {@code short}, {@code int}, {@code long}, {@code * float}, or {@code double}. * * <p> The type {@code E} in {@code Vector<E>} is the <em>boxed</em> version * of {@code ETYPE}. For example, in the type {@code Vector<Integer>}, the {@code E} * parameter is {@code Integer} and the {@code ETYPE} is {@code int}. In such a * vector, each lane carries a primitive {@code int} value. This pattern continues * for the other primitive types as well. (See also sections {@jls 5.1.7} and * {@jls 5.1.8} of the <cite>The Java Language Specification</cite>.) * * <p><a id="VLENGTH"></a> The {@linkplain #length() length} of a vector * is the lane count, the number of lanes it contains. * * This number is also called {@code VLENGTH} when the context makes * clear which vector it belongs to. Each vector has its own fixed * {@code VLENGTH} but different instances of vectors may have * different lengths. {@code VLENGTH} is an important number, because * it estimates the SIMD performance gain of a single vector operation * as compared to scalar execution of the {@code VLENGTH} scalar * operators which underly the vector operation. * * <h2><a id="species"></a>Shapes and species</h2> * * The information capacity of a vector is determined by its * {@linkplain #shape() <em>vector shape</em>}, also called its * {@code VSHAPE}. Each possible {@code VSHAPE} is represented by * a member of the {@link VectorShape} enumeration, and represents * an implementation format shared in common by all vectors of * that shape. Thus, the {@linkplain #bitSize() size in bits} of * of a vector is determined by appealing to its vector shape. * * <p> Some Java platforms give special support to only one shape, * while others support several. A typical platform is not likely * to support all the shapes described by this API. For this reason, * most vector operations work on a single input shape and * produce the same shape on output. Operations which change * shape are clearly documented as such <em>shape-changing</em>, * while the majority of operations are <em>shape-invariant</em>, * to avoid disadvantaging platforms which support only one shape. * There are queries to discover, for the current Java platform, * the {@linkplain VectorShape#preferredShape() preferred shape} * for general SIMD computation, or the * {@linkplain VectorShape#largestShapeFor(Class) largest * available shape} for any given lane type. To be portable, * code using this API should start by querying a supported * shape, and then process all data with shape-invariant * operations, within the selected shape. * * <p> Each unique combination of element type and vector shape * determines a unique * {@linkplain #species() <em>vector species</em>}. * A vector species is represented by a fixed instance of * {@link VectorSpecies VectorSpecies<E>} * shared in common by all vectors of the same shape and * {@code ETYPE}. * * <p> Unless otherwise documented, lane-wise vector operations * require that all vector inputs have exactly the same {@code VSHAPE} * and {@code VLENGTH}, which is to say that they must have exactly * the same species. This allows corresponding lanes to be paired * unambiguously. The {@link #check(VectorSpecies) check()} method * provides an easy way to perform this check explicitly. * * <p> Vector shape, {@code VLENGTH}, and {@code ETYPE} are all * mutually constrained, so that {@code VLENGTH} times the * {@linkplain #elementSize() bit-size of each lane} * must always match the bit-size of the vector's shape. * * Thus, {@linkplain #reinterpretShape(VectorSpecies,int) reinterpreting} a * vector may double its length if and only if it either halves the lane size, * or else changes the shape. Likewise, reinterpreting a vector may double the * lane size if and only if it either halves the length, or else changes the * shape of the vector. * * <h2><a id="subtypes"></a>Vector subtypes</h2> * * Vector declares a set of vector operations (methods) that are common to all * element types (such as addition). Sub-classes of Vector with a concrete * element type declare further operations that are specific to that * element type (such as access to element values in lanes, logical operations * on values of integral elements types, or transcendental operations on values * of floating point element types). * There are six abstract sub-classes of Vector corresponding to the supported set * of element types, {@link ByteVector}, {@link ShortVector}, * {@link IntVector}, {@link LongVector}, {@link FloatVector}, and * {@link DoubleVector}. Along with type-specific operations these classes * support creation of vector values (instances of Vector). * They expose static constants corresponding to the supported species, * and static methods on these types generally take a species as a parameter. * For example, * {@link FloatVector#fromArray(VectorSpecies, float[], int) FloatVector.fromArray} * creates and returns a float vector of the specified species, with elements * loaded from the specified float array. * It is recommended that Species instances be held in {@code static final} * fields for optimal creation and usage of Vector values by the runtime compiler. * * <p> As an example of static constants defined by the typed vector classes, * constant {@link FloatVector#SPECIES_256 FloatVector.SPECIES_256} * is the unique species whose lanes are {@code float}s and whose * vector size is 256 bits. Again, the constant * {@link FloatVector#SPECIES_PREFERRED} is the species which * best supports processing of {@code float} vector lanes on * the currently running Java platform. * * <p> As another example, a broadcast scalar value of * {@code (double)0.5} can be obtained by calling * {@link DoubleVector#broadcast(VectorSpecies,double) * DoubleVector.broadcast(dsp, 0.5)}, but the argument {@code dsp} is * required to select the species (and hence the shape and length) of * the resulting vector. * * <h2><a id="lane-wise"></a>Lane-wise operations</h2> * * We use the term <em>lanes</em> when defining operations on * vectors. The number of lanes in a vector is the number of scalar * elements it holds. For example, a vector of type {@code float} and * shape {@code S_256_BIT} has eight lanes, since {@code 32*8=256}. * * <p> Most operations on vectors are lane-wise, which means the operation * is composed of an underlying scalar operator, which is repeated for * each distinct lane of the input vector. If there are additional * vector arguments of the same type, their lanes are aligned with the * lanes of the first input vector. (They must all have a common * {@code VLENGTH}.) For most lane-wise operations, the output resulting * from a lane-wise operation will have a {@code VLENGTH} which is equal to * the {@code VLENGTH} of the input(s) to the operation. Thus, such lane-wise * operations are <em>length-invariant</em>, in their basic definitions. * * <p> The principle of length-invariance is combined with another * basic principle, that most length-invariant lane-wise operations are also * <em>shape-invariant</em>, meaning that the inputs and the output of * a lane-wise operation will have a common {@code VSHAPE}. When the * principles conflict, because a logical result (with an invariant * {@code VLENGTH}), does not fit into the invariant {@code VSHAPE}, * the resulting expansions and contractions are handled explicitly * with * <a href="Vector.html#expansion">special conventions</a>. * * <p> Vector operations can be grouped into various categories and * their behavior can be generally specified in terms of underlying * scalar operators. In the examples below, {@code ETYPE} is the * element type of the operation (such as {@code int.class}) and * {@code EVector} is the corresponding concrete vector type (such as * {@code IntVector.class}). * * <ul> * <li> * A <em>lane-wise unary</em> operation, such as * {@code w = v0.}{@link Vector#neg() neg}{@code ()}, * takes one input vector, * distributing a unary scalar operator across the lanes, * and produces a result vector of the same type and shape. * * For each lane of the input vector {@code a}, * the underlying scalar operator is applied to the lane value. * The result is placed into the vector result in the same lane. * The following pseudocode illustrates the behavior of this operation * category: * * <pre>{@code * ETYPE scalar_unary_op(ETYPE s); * EVector a = ...; * VectorSpecies<E> species = a.species(); * ETYPE[] ar = new ETYPE[a.length()]; * for (int i = 0; i < ar.length; i++) { * ar[i] = scalar_unary_op(a.lane(i)); * } * EVector r = EVector.fromArray(species, ar, 0); * }</pre> * * <li> * A <em>lane-wise binary</em> operation, such as * {@code w = v0.}{@link Vector#add(Vector) add}{@code (v1)}, * takes two input vectors, * distributing a binary scalar operator across the lanes, * and produces a result vector of the same type and shape. * * For each lane of the two input vectors {@code a} and {@code b}, * the underlying scalar operator is applied to the lane values. * The result is placed into the vector result in the same lane. * The following pseudocode illustrates the behavior of this operation * category: * * <pre>{@code * ETYPE scalar_binary_op(ETYPE s, ETYPE t); * EVector a = ...; * VectorSpecies<E> species = a.species(); * EVector b = ...; * b.check(species); // must have same species * ETYPE[] ar = new ETYPE[a.length()]; * for (int i = 0; i < ar.length; i++) { * ar[i] = scalar_binary_op(a.lane(i), b.lane(i)); * } * EVector r = EVector.fromArray(species, ar, 0); * }</pre> * </li> * * <li> * Generalizing from unary and binary operations, * a <em>lane-wise n-ary</em> operation takes {@code N} input vectors {@code v[j]}, * distributing an n-ary scalar operator across the lanes, * and produces a result vector of the same type and shape. * Except for a few ternary operations, such as * {@code w = v0.}{@link FloatVector#fma(Vector,Vector) fma}{@code (v1,v2)}, * this API has no support for * lane-wise n-ary operations. * * For each lane of all of the input vectors {@code v[j]}, * the underlying scalar operator is applied to the lane values. * The result is placed into the vector result in the same lane. * The following pseudocode illustrates the behavior of this operation * category: * * <pre>{@code * ETYPE scalar_nary_op(ETYPE... args); * EVector[] v = ...; * int N = v.length; * VectorSpecies<E> species = v[0].species(); * for (EVector arg : v) { * arg.check(species); // all must have same species * } * ETYPE[] ar = new ETYPE[a.length()]; * for (int i = 0; i < ar.length; i++) { * ETYPE[] args = new ETYPE[N]; * for (int j = 0; j < N; j++) { * args[j] = v[j].lane(i); * } * ar[i] = scalar_nary_op(args); * } * EVector r = EVector.fromArray(species, ar, 0); * }</pre> * </li> * * <li> * A <em>lane-wise conversion</em> operation, such as * {@code w0 = v0.}{@link * Vector#convert(VectorOperators.Conversion,int) * convert}{@code (VectorOperators.I2D, 0)}, * takes one input vector, * distributing a unary scalar conversion operator across the lanes, * and produces a logical result of the converted values. The logical * result (or at least a part of it) is presented in a vector of the * same shape as the input vector. * * <p> Unlike other lane-wise operations, conversions can change lane * type, from the input (domain) type to the output (range) type. The * lane size may change along with the type. In order to manage the * size changes, lane-wise conversion methods can product <em>partial * results</em>, under the control of a {@code part} parameter, which * is <a href="Vector.html#expansion">explained elsewhere</a>. * (Following the example above, the second group of converted lane * values could be obtained as * {@code w1 = v0.convert(VectorOperators.I2D, 1)}.) * * <p> The following pseudocode illustrates the behavior of this * operation category in the specific example of a conversion from * {@code int} to {@code double}, retaining either lower or upper * lanes (depending on {@code part}) to maintain shape-invariance: * * <pre>{@code * IntVector a = ...; * int VLENGTH = a.length(); * int part = ...; // 0 or 1 * VectorShape VSHAPE = a.shape(); * double[] arlogical = new double[VLENGTH]; * for (int i = 0; i < limit; i++) { * int e = a.lane(i); * arlogical[i] = (double) e; * } * VectorSpecies<Double> rs = VSHAPE.withLanes(double.class); * int M = Double.BITS / Integer.BITS; // expansion factor * int offset = part * (VLENGTH / M); * DoubleVector r = DoubleVector.fromArray(rs, arlogical, offset); * assert r.length() == VLENGTH / M; * }</pre> * </li> * * <li> * A <em>cross-lane reduction</em> operation, such as * {@code e = v0.}{@link * IntVector#reduceLanes(VectorOperators.Associative) * reduceLanes}{@code (VectorOperators.ADD)}, * operates on all * the lane elements of an input vector. * An accumulation function is applied to all the * lane elements to produce a scalar result. * If the reduction operation is associative then the result may be accumulated * by operating on the lane elements in any order using a specified associative * scalar binary operation and identity value. Otherwise, the reduction * operation specifies the order of accumulation. * The following pseudocode illustrates the behavior of this operation category * if it is associative: * <pre>{@code * ETYPE assoc_scalar_binary_op(ETYPE s, ETYPE t); * EVector a = ...; * ETYPE r = <identity value>; * for (int i = 0; i < a.length(); i++) { * r = assoc_scalar_binary_op(r, a.lane(i)); * } * }</pre> * </li> * * <li> * A <em>cross-lane movement</em> operation, such as * {@code w = v0.}{@link * Vector#rearrange(VectorShuffle) rearrange}{@code (shuffle)} * operates on all * the lane elements of an input vector and moves them * in a data-dependent manner into <em>different lanes</em> * in an output vector. * The movement is steered by an auxiliary datum, such as * a {@link VectorShuffle} or a scalar index defining the * origin of the movement. * The following pseudocode illustrates the behavior of this * operation category, in the case of a shuffle: * <pre>{@code * EVector a = ...; * Shuffle<E> s = ...; * ETYPE[] ar = new ETYPE[a.length()]; * for (int i = 0; i < ar.length; i++) { * int source = s.laneSource(i); * ar[i] = a.lane(source); * } * EVector r = EVector.fromArray(a.species(), ar, 0); * }</pre> * </li> * * <li> * A <em>masked operation</em> is one which is a variation on one of the * previous operations (either lane-wise or cross-lane), where * the operation takes an extra trailing {@link VectorMask} argument. * In lanes the mask is set, the operation behaves as if the mask * argument were absent, but in lanes where the mask is unset, the * underlying scalar operation is suppressed. * Masked operations are explained in * <a href="Vector.html#masking">greater detail elsewhere</a>. * </li> * * <li> * A very special case of a masked lane-wise binary operation is a * {@linkplain #blend(Vector,VectorMask) blend}, which operates * lane-wise on two input vectors {@code a} and {@code b}, selecting lane * values from one input or the other depending on a mask {@code m}. * In lanes where {@code m} is set, the corresponding value from * {@code b} is selected into the result; otherwise the value from * {@code a} is selected. Thus, a blend acts as a vectorized version * of Java's ternary selection expression {@code m?b:a}: * <pre>{@code * ETYPE[] ar = new ETYPE[a.length()]; * for (int i = 0; i < ar.length; i++) { * boolean isSet = m.laneIsSet(i); * ar[i] = isSet ? b.lane(i) : a.lane(i); * } * EVector r = EVector.fromArray(species, ar, 0); * }</pre> * </li> * * <li> * A <em>lane-wise binary test</em> operation, such as * {@code m = v0.}{@link Vector#lt(Vector) lt}{@code (v1)}, * takes two input vectors, * distributing a binary scalar comparison across the lanes, * and produces, not a vector of booleans, but rather a * {@linkplain VectorMask vector mask}. * * For each lane of the two input vectors {@code a} and {@code b}, * the underlying scalar comparison operator is applied to the lane values. * The resulting boolean is placed into the vector mask result in the same lane. * The following pseudocode illustrates the behavior of this operation * category: * <pre>{@code * boolean scalar_binary_test_op(ETYPE s, ETYPE t); * EVector a = ...; * VectorSpecies<E> species = a.species(); * EVector b = ...; * b.check(species); // must have same species * boolean[] mr = new boolean[a.length()]; * for (int i = 0; i < mr.length; i++) { * mr[i] = scalar_binary_test_op(a.lane(i), b.lane(i)); * } * VectorMask<E> m = VectorMask.fromArray(species, mr, 0); * }</pre> * </li> * * <li> * Similarly to a binary comparison, a <em>lane-wise unary test</em> * operation, such as * {@code m = v0.}{@link Vector#test(VectorOperators.Test) * test}{@code (IS_FINITE)}, * takes one input vector, distributing a scalar predicate * (a test function) across the lanes, and produces a * {@linkplain VectorMask vector mask}. * </li> * * </ul> * * <p> * If a vector operation does not belong to one of the above categories then * the method documentation explicitly specifies how it processes the lanes of * input vectors, and where appropriate illustrates the behavior using * pseudocode. * * <p> * Most lane-wise binary and comparison operations offer convenience * overloadings which accept a scalar as the second input, in place of a * vector. In this case the scalar value is promoted to a vector by * {@linkplain Vector#broadcast(long) broadcasting it} * into the same lane structure as the first input. * * For example, to multiply all lanes of a {@code double} vector by * a scalar value {@code 1.1}, the expression {@code v.mul(1.1)} is * easier to work with than an equivalent expression with an explicit * broadcast operation, such as {@code v.mul(v.broadcast(1.1))} * or {@code v.mul(DoubleVector.broadcast(v.species(), 1.1))}. * * Unless otherwise specified the scalar variant always behaves as if * each scalar value is first transformed to a vector of the same * species as the first vector input, using the appropriate * {@code broadcast} operation. * * <h2><a id="masking"></a>Masked operations</h2> * * <p> Many vector operations accept an optional * {@link VectorMask mask} argument, selecting which lanes participate * in the underlying scalar operator. If present, the mask argument * appears at the end of the method argument list. * * <p> Each lane of the mask argument is a boolean which is either in * the <em>set</em> or <em>unset</em> state. For lanes where the mask * argument is unset, the underlying scalar operator is suppressed. * In this way, masks allow vector operations to emulate scalar * control flow operations, without losing SIMD parallelism, except * where the mask lane is unset. * * <p> An operation suppressed by a mask will never cause an exception * or side effect of any sort, even if the underlying scalar operator * can potentially do so. For example, an unset lane that seems to * access an out of bounds array element or divide an integral value * by zero will simply be ignored. Values in suppressed lanes never * participate or appear in the result of the overall operation. * * <p> Result lanes corresponding to a suppressed operation will be * filled with a default value which depends on the specific * operation, as follows: * * <ul> * * <li>If the masked operation is a unary, binary, or n-ary arithmetic or * logical operation, suppressed lanes are filled from the first * vector operand (i.e., the vector receiving the method call), as if * by a {@linkplain #blend(Vector,VectorMask) blend}.</li> * * <li>If the masked operation is a memory load or a {@code slice()} from * another vector, suppressed lanes are not loaded, and are filled * with the default value for the {@code ETYPE}, which in every case * consists of all zero bits. An unset lane can never cause an * exception, even if the hypothetical corresponding memory location * does not exist (because it is out of an array's index range).</li> * * <li>If the operation is a cross-lane operation with an operand * which supplies lane indexes (of type {@code VectorShuffle} or * {@code Vector}, suppressed lanes are not computed, and are filled * with the zero default value. Normally, invalid lane indexes elicit * an {@code IndexOutOfBoundsException}, but if a lane is unset, the * zero value is quietly substituted, regardless of the index. This * rule is similar to the previous rule, for masked memory loads.</li> * * <li>If the masked operation is a memory store or an {@code unslice()} into * another vector, suppressed lanes are not stored, and the * corresponding memory or vector locations (if any) are unchanged. * * <p> (Note: Memory effects such as race conditions never occur for * suppressed lanes. That is, implementations will not secretly * re-write the existing value for unset lanes. In the Java Memory * Model, reassigning a memory variable to its current value is not a * no-op; it may quietly undo a racing store from another * thread.)</p> * </li> * * <li>If the masked operation is a reduction, suppressed lanes are ignored * in the reduction. If all lanes are suppressed, a suitable neutral * value is returned, depending on the specific reduction operation, * and documented by the masked variant of that method. (This means * that users can obtain the neutral value programmatically by * executing the reduction on a dummy vector with an all-unset mask.) * * <li>If the masked operation is a comparison operation, suppressed output * lanes in the resulting mask are themselves unset, as if the * suppressed comparison operation returned {@code false} regardless * of the suppressed input values. In effect, it is as if the * comparison operation were performed unmasked, and then the * result intersected with the controlling mask.</li> * * <li>In other cases, such as masked * <a href="Vector.html#cross-lane"><em>cross-lane movements</em></a>, * the specific effects of masking are documented by the masked * variant of the method. * * </ul> * * <p> As an example, a masked binary operation on two input vectors * {@code a} and {@code b} suppresses the binary operation for lanes * where the mask is unset, and retains the original lane value from * {@code a}. The following pseudocode illustrates this behavior: * <pre>{@code * ETYPE scalar_binary_op(ETYPE s, ETYPE t); * EVector a = ...; * VectorSpecies<E> species = a.species(); * EVector b = ...; * b.check(species); // must have same species * VectorMask<E> m = ...; * m.check(species); // must have same species * boolean[] ar = new boolean[a.length()]; * for (int i = 0; i < ar.length; i++) { * if (m.laneIsSet(i)) { * ar[i] = scalar_binary_op(a.lane(i), b.lane(i)); * } else { * ar[i] = a.lane(i); // from first input * } * } * EVector r = EVector.fromArray(species, ar, 0); * }</pre> * * <h2><a id="lane-order"></a>Lane order and byte order</h2> * * The number of lane values stored in a given vector is referred to * as its {@linkplain #length() vector length} or {@code VLENGTH}. * * It is useful to consider vector lanes as ordered * <em>sequentially</em> from first to last, with the first lane * numbered {@code 0}, the next lane numbered {@code 1}, and so on to * the last lane numbered {@code VLENGTH-1}. This is a temporal * order, where lower-numbered lanes are considered earlier than * higher-numbered (later) lanes. This API uses these terms * in preference to spatial terms such as "left", "right", "high", * and "low". * * <p> Temporal terminology works well for vectors because they * (usually) represent small fixed-sized segments in a long sequence * of workload elements, where the workload is conceptually traversed * in time order from beginning to end. (This is a mental model: it * does not exclude multicore divide-and-conquer techniques.) Thus, * when a scalar loop is transformed into a vector loop, adjacent * scalar items (one earlier, one later) in the workload end up as * adjacent lanes in a single vector (again, one earlier, one later). * At a vector boundary, the last lane item in the earlier vector is * adjacent to (and just before) the first lane item in the * immediately following vector. * * <p> Vectors are also sometimes thought of in spatial terms, where * the first lane is placed at an edge of some virtual paper, and * subsequent lanes are presented in order next to it. When using * spatial terms, all directions are equally plausible: Some vector * notations present lanes from left to right, and others from right * to left; still others present from top to bottom or vice versa. * Using the language of time (before, after, first, last) instead of * space (left, right, high, low) is often more likely to avoid * misunderstandings. * * <p> As second reason to prefer temporal to spatial language about * vector lanes is the fact that the terms "left", "right", "high" and * "low" are widely used to describe the relations between bits in * scalar values. The leftmost or highest bit in a given type is * likely to be a sign bit, while the rightmost or lowest bit is * likely to be the arithmetically least significant, and so on. * Applying these terms to vector lanes risks confusion, however, * because it is relatively rare to find algorithms where, given two * adjacent vector lanes, one lane is somehow more arithmetically * significant than its neighbor, and even in those cases, there is no * general way to know which neighbor is the the more significant. * * <p> Putting the terms together, we view the information structure * of a vector as a temporal sequence of lanes ("first", "next", * "earlier", "later", "last", etc.) of bit-strings which are * internally ordered spatially (either "low" to "high" or "right" to * "left"). The primitive values in the lanes are decoded from these * bit-strings, in the usual way. Most vector operations, like most * Java scalar operators, treat primitive values as atomic values, but * some operations reveal the internal bit-string structure. * * <p> When a vector is loaded from or stored into memory, the order * of vector lanes is <em>always consistent </em> with the inherent * ordering of the memory container. This is true whether or not * individual lane elements are subject to "byte swapping" due to * details of byte order. Thus, while the scalar lane elements of * vector might be "byte swapped", the lanes themselves are never * reordered, except by an explicit method call that performs * cross-lane reordering. * * <p> When vector lane values are stored to Java variables of the * same type, byte swapping is performed if and only if the * implementation of the vector hardware requires such swapping. It * is therefore unconditional and invisible. * * <p> As a useful fiction, this API presents a consistent illusion * that vector lane bytes are composed into larger lane scalars in * <em>little endian order</em>. This means that storing a vector * into a Java byte array will reveal the successive bytes of the * vector lane values in little-endian order on all platforms, * regardless of native memory order, and also regardless of byte * order (if any) within vector unit registers. * * <p> This hypothetical little-endian ordering also appears when a * {@linkplain #reinterpretShape(VectorSpecies,int) reinterpretation cast} is * applied in such a way that lane boundaries are discarded and * redrawn differently, while maintaining vector bits unchanged. In * such an operation, two adjacent lanes will contribute bytes to a * single new lane (or vice versa), and the sequential order of the * two lanes will determine the arithmetic order of the bytes in the * single lane. In this case, the little-endian convention provides * portable results, so that on all platforms earlier lanes tend to * contribute lower (rightward) bits, and later lanes tend to * contribute higher (leftward) bits. The {@linkplain #reinterpretAsBytes() * reinterpretation casts} between {@link ByteVector}s and the * other non-byte vectors use this convention to clarify their * portable semantics. * * <p> The little-endian fiction for relating lane order to per-lane * byte order is slightly preferable to an equivalent big-endian * fiction, because some related formulas are much simpler, * specifically those which renumber bytes after lane structure * changes. The earliest byte is invariantly earliest across all lane * structure changes, but only if little-endian convention are used. * The root cause of this is that bytes in scalars are numbered from * the least significant (rightmost) to the most significant * (leftmost), and almost never vice-versa. If we habitually numbered * sign bits as zero (as on some computers) then this API would reach * for big-endian fictions to create unified addressing of vector * bytes. * * <h2><a id="memory"></a>Memory operations</h2> * * As was already mentioned, vectors can be loaded from memory and * stored back. An optional mask can control which individual memory * locations are read from or written to. The shape of a vector * determines how much memory it will occupy. * * An implementation typically has the property, in the absence of * masking, that lanes are stored as a dense sequence of back-to-back * values in memory, the same as a dense (gap-free) series of single * scalar values in an array of the scalar type. * * In such cases memory order corresponds exactly to lane order. The * first vector lane value occupies the first position in memory, and so on, * up to the length of the vector. Further, the memory order of stored * vector lanes corresponds to increasing index values in a Java array or * in a {@link java.nio.ByteBuffer}. * * <p> Byte order for lane storage is chosen such that the stored * vector values can be read or written as single primitive values, * within the array or buffer that holds the vector, producing the * same values as the lane-wise values within the vector. * This fact is independent of the convenient fiction that lane values * inside of vectors are stored in little-endian order. * * <p> For example, * {@link FloatVector#fromArray(VectorSpecies, float[], int) * FloatVector.fromArray(fsp,fa,i)} * creates and returns a float vector of some particular species {@code fsp}, * with elements loaded from some float array {@code fa}. * The first lane is loaded from {@code fa[i]} and the last lane * is initialized loaded from {@code fa[i+VL-1]}, where {@code VL} * is the length of the vector as derived from the species {@code fsp}. * Then, {@link FloatVector#add(Vector) fv=fv.add(fv2)} * will produce another float vector of that species {@code fsp}, * given a vector {@code fv2} of the same species {@code fsp}. * Next, {@link FloatVector#compare(VectorOperators.Comparison,float) * mnz=fv.compare(NE, 0.0f)} tests whether the result is zero, * yielding a mask {@code mnz}. The non-zero lanes (and only those * lanes) can then be stored back into the original array elements * using the statement * {@link FloatVector#intoArray(float[],int,VectorMask) fv.intoArray(fa,i,mnz)}. * * <h2><a id="expansion"></a>Expansions, contractions, and partial results</h2> * * Since vectors are fixed in size, occasions often arise where the * logical result of an operation is not the same as the physical size * of the proposed output vector. To encourage user code that is as * portable and predictable as possible, this API has a systematic * approach to the design of such <em>resizing</em> vector operations. * * <p> As a basic principle, lane-wise operations are * <em>length-invariant</em>, unless clearly marked otherwise. * Length-invariance simply means that * if {@code VLENGTH} lanes go into an operation, the same number * of lanes come out, with nothing discarded and no extra padding. * * <p> As a second principle, sometimes in tension with the first, * lane-wise operations are also <em>shape-invariant</em>, unless * clearly marked otherwise. * * Shape-invariance means that {@code VSHAPE} is constant for typical * computations. Keeping the same shape throughout a computation * helps ensure that scarce vector resources are efficiently used. * (On some hardware platforms shape changes could cause unwanted * effects like extra data movement instructions, round trips through * memory, or pipeline bubbles.) * * <p> Tension between these principles arises when an operation * produces a <em>logical result</em> that is too large for the * required output {@code VSHAPE}. In other cases, when a logical * result is smaller than the capacity of the output {@code VSHAPE}, * the positioning of the logical result is open to question, since * the physical output vector must contain a mix of logical result and * padding. * * <p> In the first case, of a too-large logical result being crammed * into a too-small output {@code VSHAPE}, we say that data has * <em>expanded</em>. In other words, an <em>expansion operation</em> * has caused the output shape to overflow. Symmetrically, in the * second case of a small logical result fitting into a roomy output * {@code VSHAPE}, the data has <em>contracted</em>, and the * <em>contraction operation</em> has required the output shape to pad * itself with extra zero lanes. * * <p> In both cases we can speak of a parameter {@code M} which * measures the <em>expansion ratio</em> or <em>contraction ratio</em> * between the logical result size (in bits) and the bit-size of the * actual output shape. When vector shapes are changed, and lane * sizes are not, {@code M} is just the integral ratio of the output * shape to the logical result. (With the possible exception of * the {@linkplain VectorShape#S_Max_BIT maximum shape}, all vector * sizes are powers of two, and so the ratio {@code M} is always * an integer. In the hypothetical case of a non-integral ratio, * the value {@code M} would be rounded up to the next integer, * and then the same general considerations would apply.) * * <p> If the logical result is larger than the physical output shape, * such a shape change must inevitably drop result lanes (all but * {@code 1/M} of the logical result). If the logical size is smaller * than the output, the shape change must introduce zero-filled lanes * of padding (all but {@code 1/M} of the physical output). The first * case, with dropped lanes, is an expansion, while the second, with * padding lanes added, is a contraction. * * <p> Similarly, consider a lane-wise conversion operation which * leaves the shape invariant but changes the lane size by a ratio of * {@code M}. If the logical result is larger than the output (or * input), this conversion must reduce the {@code VLENGTH} lanes of the * output by {@code M}, dropping all but {@code 1/M} of the logical * result lanes. As before, the dropping of lanes is the hallmark of * an expansion. A lane-wise operation which contracts lane size by a * ratio of {@code M} must increase the {@code VLENGTH} by the same * factor {@code M}, filling the extra lanes with a zero padding * value; because padding must be added this is a contraction. * * <p> It is also possible (though somewhat confusing) to change both * lane size and container size in one operation which performs both * lane conversion <em>and</em> reshaping. If this is done, the same * rules apply, but the logical result size is the product of the * input size times any expansion or contraction ratio from the lane * change size. * * <p> For completeness, we can also speak of <em>in-place * operations</em> for the frequent case when resizing does not occur. * With an in-place operation, the data is simply copied from logical * output to its physical container with no truncation or padding. * The ratio parameter {@code M} in this case is unity. * * <p> Note that the classification of contraction vs. expansion * depends on the relative sizes of the logical result and the * physical output container. The size of the input container may be * larger or smaller than either of the other two values, without * changing the classification. For example, a conversion from a * 128-bit shape to a 256-bit shape will be a contraction in many * cases, but it would be an expansion if it were combined with a * conversion from {@code byte} to {@code long}, since in that case * the logical result would be 1024 bits in size. This example also * illustrates that a logical result does not need to correspond to * any particular platform-supported vector shape. * * <p> Although lane-wise masked operations can be viewed as producing * partial operations, they are not classified (in this API) as * expansions or contractions. A masked load from an array surely * produces a partial vector, but there is no meaningful "logical * output vector" that this partial result was contracted from. * * <p> Some care is required with these terms, because it is the * <em>data</em>, not the <em>container size</em>, that is expanding * or contracting, relative to the size of its output container. * Thus, resizing a 128-bit input into 512-bit vector has the effect * of a <em>contraction</em>. Though the 128 bits of payload hasn't * changed in size, we can say it "looks smaller" in its new 512-bit * home, and this will capture the practical details of the situation. * * <p> If a vector method might expand its data, it accepts an extra * {@code int} parameter called {@code part}, or the "part number". * The part number must be in the range {@code [0..M-1]}, where * {@code M} is the expansion ratio. The part number selects one * of {@code M} contiguous disjoint equally-sized blocks of lanes * from the logical result and fills the physical output vector * with this block of lanes. * * <p> Specifically, the lanes selected from the logical result of an * expansion are numbered in the range {@code [R..R+L-1]}, where * {@code L} is the {@code VLENGTH} of the physical output vector, and * the origin of the block, {@code R}, is {@code part*L}. * * <p> A similar convention applies to any vector method that might * contract its data. Such a method also accepts an extra part number * parameter (again called {@code part}) which steers the contracted * data lanes one of {@code M} contiguous disjoint equally-sized * blocks of lanes in the physical output vector. The remaining lanes * are filled with zero, or as specified by the method. * * <p> Specifically, the data is steered into the lanes numbered in the * range {@code [R..R+L-1]}, where {@code L} is the {@code VLENGTH} of * the logical result vector, and the origin of the block, {@code R}, * is again a multiple of {@code L} selected by the part number, * specifically {@code |part|*L}. * * <p> In the case of a contraction, the part number must be in the * non-positive range {@code [-M+1..0]}. This convention is adopted * because some methods can perform both expansions and contractions, * in a data-dependent manner, and the extra sign on the part number * serves as an error check. If vector method takes a part number and * is invoked to perform an in-place operation (neither contracting * nor expanding), the {@code part} parameter must be exactly zero. * Part numbers outside the allowed ranges will elicit an indexing * exception. Note that in all cases a zero part number is valid, and * corresponds to an operation which preserves as many lanes as * possible from the beginning of the logical result, and places them * into the beginning of the physical output container. This is * often a desirable default, so a part number of zero is safe * in all cases and useful in most cases. * * <p> The various resizing operations of this API contract or expand * their data as follows: * <ul> * * <li> * {@link Vector#convert(VectorOperators.Conversion,int) Vector.convert()} * will expand (respectively, contract) its operand by ratio * {@code M} if the * {@linkplain #elementSize() element size} of its output is * larger (respectively, smaller) by a factor of {@code M}. * If the element sizes of input and output are the same, * then {@code convert()} is an in-place operation. * * <li> * {@link Vector#convertShape(VectorOperators.Conversion,VectorSpecies,int) Vector.convertShape()} * will expand (respectively, contract) its operand by ratio * {@code M} if the bit-size of its logical result is * larger (respectively, smaller) than the bit-size of its * output shape. * The size of the logical result is defined as the * {@linkplain #elementSize() element size} of the output, * times the {@code VLENGTH} of its input. * * Depending on the ratio of the changed lane sizes, the logical size * may be (in various cases) either larger or smaller than the input * vector, independently of whether the operation is an expansion * or contraction. * * <li> * Since {@link Vector#castShape(VectorSpecies,int) Vector.castShape()} * is a convenience method for {@code convertShape()}, its classification * as an expansion or contraction is the same as for {@code convertShape()}. * * <li> * {@link Vector#reinterpretShape(VectorSpecies,int) Vector.reinterpretShape()} * is an expansion (respectively, contraction) by ratio {@code M} if the * {@linkplain #bitSize() vector bit-size} of its input is * crammed into a smaller (respectively, dropped into a larger) * output container by a factor of {@code M}. * Otherwise it is an in-place operation. * * Since this method is a reinterpretation cast that can erase and * redraw lane boundaries as well as modify shape, the input vector's * lane size and lane count are irrelevant to its classification as * expanding or contracting. * * <li> * The {@link #unslice(int,Vector,int) unslice()} methods expand * by a ratio of {@code M=2}, because the single input slice is * positioned and inserted somewhere within two consecutive background * vectors. The part number selects the first or second background * vector, as updated by the inserted slice. * Note that the corresponding * {@link #slice(int,Vector) slice()} methods, although inverse * to the {@code unslice()} methods, do not contract their data * and thus require no part number. This is because * {@code slice()} delivers a slice of exactly {@code VLENGTH} * lanes extracted from two input vectors. * </ul> * * The method {@link VectorSpecies#partLimit(VectorSpecies,boolean) * partLimit()} on {@link VectorSpecies} can be used, before any * expanding or contracting operation is performed, to query the * limiting value on a part parameter for a proposed expansion * or contraction. The value returned from {@code partLimit()} is * positive for expansions, negative for contractions, and zero for * in-place operations. Its absolute value is the parameter {@code * M}, and so it serves as an exclusive limit on valid part number * arguments for the relevant methods. Thus, for expansions, the * {@code partLimit()} value {@code M} is the exclusive upper limit * for part numbers, while for contractions the {@code partLimit()} * value {@code -M} is the exclusive <em>lower</em> limit. * * <h2><a id="cross-lane"></a>Moving data across lane boundaries</h2> * The cross-lane methods which do not redraw lanes or change species * are more regularly structured and easier to reason about. * These operations are: * <ul> * * <li>The {@link #slice(int,Vector) slice()} family of methods, * which extract contiguous slice of {@code VLENGTH} fields from * a given origin point within a concatenated pair of vectors. * * <li>The {@link #unslice(int,Vector,int) unslice()} family of * methods, which insert a contiguous slice of {@code VLENGTH} fields * into a concatenated pair of vectors at a given origin point. * * <li>The {@link #rearrange(VectorShuffle) rearrange()} family of * methods, which select an arbitrary set of {@code VLENGTH} lanes * from one or two input vectors, and assemble them in an arbitrary * order. The selection and order of lanes is controlled by a * {@code VectorShuffle} object, which acts as an routing table * mapping source lanes to destination lanes. A {@code VectorShuffle} * can encode a mathematical permutation as well as many other * patterns of data movement. * * </ul> * <p> Some vector operations are not lane-wise, but rather move data * across lane boundaries. Such operations are typically rare in SIMD * code, though they are sometimes necessary for specific algorithms * that manipulate data formats at a low level, and/or require SIMD * data to move in complex local patterns. (Local movement in a small * window of a large array of data is relatively unusual, although * some highly patterned algorithms call for it.) In this API such * methods are always clearly recognizable, so that simpler lane-wise * reasoning can be confidently applied to the rest of the code. * * <p> In some cases, vector lane boundaries are discarded and * "redrawn from scratch", so that data in a given input lane might * appear (in several parts) distributed through several output lanes, * or (conversely) data from several input lanes might be consolidated * into a single output lane. The fundamental method which can redraw * lanes boundaries is * {@link #reinterpretShape(VectorSpecies,int) reinterpretShape()}. * Built on top of this method, certain convenience methods such * as {@link #reinterpretAsBytes() reinterpretAsBytes()} or * {@link #reinterpretAsInts() reinterpretAsInts()} will * (potentially) redraw lane boundaries, while retaining the * same overall vector shape. * * <p> Operations which produce or consume a scalar result can be * viewed as very simple cross-lane operations. Methods in the * {@link #reduceLanesToLong(VectorOperators.Associative) * reduceLanes()} family fold together all lanes (or mask-selected * lanes) of a method and return a single result. As an inverse, the * {@link #broadcast(long) broadcast} family of methods can be thought * of as crossing lanes in the other direction, from a scalar to all * lanes of the output vector. Single-lane access methods such as * {@code lane(I)} or {@code withLane(I,E)} might also be regarded as * very simple cross-lane operations. * * <p> Likewise, a method which moves a non-byte vector to or from a * byte array could be viewed as a cross-lane operation, because the * vector lanes must be distributed into separate bytes, or (in the * other direction) consolidated from array bytes. * * @implNote * * <h2>Hardware platform dependencies and limitations</h2> * * The Vector API is to accelerate computations in style of Single * Instruction Multiple Data (SIMD), using available hardware * resources such as vector hardware registers and vector hardware * instructions. The API is designed to make effective use of * multiple SIMD hardware platforms. * * <p> This API will also work correctly even on Java platforms which * do not include specialized hardware support for SIMD computations. * The Vector API is not likely to provide any special performance * benefit on such platforms. * * <p> Currently the implementation is optimized to work best on: * * <ul> * * <li> Intel x64 platforms supporting at least AVX2 up to AVX-512. * Masking using mask registers and mask accepting hardware * instructions on AVX-512 are not currently supported. * * <li> ARM AArch64 platforms supporting NEON. Although the API has * been designed to ensure ARM SVE instructions can be supported * (vector sizes between 128 to 2048 bits) there is currently no * implementation of such instructions and the general masking * capability. * * </ul> * The implementation currently supports masked lane-wise operations * in a cross-platform manner by composing the unmasked lane-wise * operation with {@link #blend(Vector, VectorMask) blend} as in * the expression {@code a.blend(a.lanewise(op, b), m)}, where * {@code a} and {@code b} are vectors, {@code op} is the vector * operation, and {@code m} is the mask. * * <p> The implementation does not currently support optimal * vectorized instructions for floating point transcendental * functions (such as operators {@link VectorOperators#SIN SIN} * and {@link VectorOperators#LOG LOG}). * * <h2>No boxing of primitives</h2> * * Although a vector type like {@code Vector<Integer>} may seem to * work with boxed {@code Integer} values, the overheads associated * with boxing are avoided by having each vector subtype work * internally on lane values of the actual {@code ETYPE}, such as * {@code int}. * * <h2>Value-based classes and identity operations</h2> * * {@code Vector}, along with all of its subtypes and many of its * helper types like {@code VectorMask} and {@code VectorShuffle}, is a * <a href="{@docRoot}/java.base/java/lang/doc-files/ValueBased.html">value-based</a> * class. * * <p> Once created, a vector is never mutated, not even if only * {@linkplain IntVector#withLane(int,int) a single lane is changed}. * A new vector is always created to hold a new configuration * of lane values. The unavailability of mutative methods is a * necessary consequence of suppressing the object identity of * all vectors, as value-based classes. * * <p> With {@code Vector}, * * <!-- The following paragraph is shared verbatim * -- between Vector.java and package-info.java --> * identity-sensitive operations such as {@code ==} may yield * unpredictable results, or reduced performance. Oddly enough, * {@link Vector#equals(Object) v.equals(w)} is likely to be faster * than {@code v==w}, since {@code equals} is <em>not</em> an identity * sensitive method. * * Also, these objects can be stored in locals and parameters and as * {@code static final} constants, but storing them in other Java * fields or in array elements, while semantically valid, may incur * performance penalties. * <!-- The preceding paragraph is shared verbatim * -- between Vector.java and package-info.java --> * * @param <E> the boxed version of {@code ETYPE}, * the element type of a vector * */ @SuppressWarnings("exports") public abstract class Vector<E> extends jdk.internal.vm.vector.VectorSupport.Vector<E> { // This type is sealed within its package. // Users cannot roll their own vector types. Vector(Object bits) { super(bits); } /** * Returns the species of this vector. * * @return the species of this vector */ public abstract VectorSpecies<E> species(); /** * Returns the primitive <a href="Vector.html#ETYPE">element type</a> * ({@code ETYPE}) of this vector. * * @implSpec * This is the same value as {@code this.species().elementType()}. * * @return the primitive element type of this vector */ public abstract Class<E> elementType(); /** * Returns the size of each lane, in bits, of this vector. * * @implSpec * This is the same value as {@code this.species().elementSize()}. * * @return the lane size, in bits, of this vector */ public abstract int elementSize(); /** * Returns the shape of this vector. * * @implSpec * This is the same value as {@code this.species().vectorShape()}. * * @return the shape of this vector */ public abstract VectorShape shape(); /** * Returns the lane count, or <a href="Vector.html#VLENGTH">vector length</a> * ({@code VLENGTH}). * * @return the lane count */ public abstract int length(); /** * Returns the total size, in bits, of this vector. * * @implSpec * This is the same value as {@code this.shape().vectorBitSize()}. * * @return the total size, in bits, of this vector */ public abstract int bitSize(); /** * Returns the total size, in bytes, of this vector. * * @implSpec * This is the same value as {@code this.bitSize()/Byte.SIZE}. * * @return the total size, in bytes, of this vector */ public abstract int byteSize(); /// Arithmetic /** * Operates on the lane values of this vector. * * This is a <a href="Vector.html#lane-wise">lane-wise</a> * unary operation which applies * the selected operation to each lane. * * @apiNote * Subtypes improve on this method by sharpening * the method return type. * * @param op the operation used to process lane values * @return the result of applying the operation lane-wise * to the input vector * @throws UnsupportedOperationException if this vector does * not support the requested operation * @see VectorOperators#NEG * @see VectorOperators#NOT * @see VectorOperators#SIN * @see #lanewise(VectorOperators.Unary,VectorMask) * @see #lanewise(VectorOperators.Binary,Vector) * @see #lanewise(VectorOperators.Ternary,Vector,Vector) */ public abstract Vector<E> lanewise(VectorOperators.Unary op); /** * Operates on the lane values of this vector, * with selection of lane elements controlled by a mask. * * This is a lane-wise unary operation which applies * the selected operation to each lane. * * @apiNote * Subtypes improve on this method by sharpening * the method return type. * * @param op the operation used to process lane values * @param m the mask controlling lane selection * @return the result of applying the operation lane-wise * to the input vector * @throws UnsupportedOperationException if this vector does * not support the requested operation * @see #lanewise(VectorOperators.Unary) */ public abstract Vector<E> lanewise(VectorOperators.Unary op, VectorMask<E> m); /** * Combines the corresponding lane values of this vector * with those of a second input vector. * * This is a <a href="Vector.html#lane-wise">lane-wise</a> * binary operation which applies * the selected operation to each lane. * * @apiNote * Subtypes improve on this method by sharpening * the method return type. * * @param op the operation used to combine lane values * @param v the input vector * @return the result of applying the operation lane-wise * to the two input vectors * @throws UnsupportedOperationException if this vector does * not support the requested operation * @see VectorOperators#ADD * @see VectorOperators#XOR * @see VectorOperators#ATAN2 * @see #lanewise(VectorOperators.Binary,Vector,VectorMask) * @see #lanewise(VectorOperators.Unary) * @see #lanewise(VectorOperators.Ternary,Vector, Vector) */ public abstract Vector<E> lanewise(VectorOperators.Binary op, Vector<E> v); /** * Combines the corresponding lane values of this vector * with those of a second input vector, * with selection of lane elements controlled by a mask. * * This is a lane-wise binary operation which applies * the selected operation to each lane. * * @apiNote * Subtypes improve on this method by sharpening * the method return type. * * @param op the operation used to combine lane values * @param v the second input vector * @param m the mask controlling lane selection * @return the result of applying the operation lane-wise * to the two input vectors * @throws UnsupportedOperationException if this vector does * not support the requested operation * @see #lanewise(VectorOperators.Binary,Vector) */ public abstract Vector<E> lanewise(VectorOperators.Binary op, Vector<E> v, VectorMask<E> m); /** * Combines the lane values of this vector * with the value of a broadcast scalar. * * This is a lane-wise binary operation which applies * the selected operation to each lane. * The return value will be equal to this expression: * {@code this.lanewise(op, this.broadcast(e))}. * * @apiNote * The {@code long} value {@code e} must be accurately * representable by the {@code ETYPE} of this vector's species, * so that {@code e==(long)(ETYPE)e}. This rule is enforced * by the implicit call to {@code broadcast()}. * <p> * Subtypes improve on this method by sharpening * the method return type and * the type of the scalar parameter {@code e}. * * @param op the operation used to combine lane values * @param e the input scalar * @return the result of applying the operation lane-wise * to the input vector and the scalar * @throws UnsupportedOperationException if this vector does * not support the requested operation * @throws IllegalArgumentException * if the given {@code long} value cannot * be represented by the right operand type * of the vector operation * @see #broadcast(long) * @see #lanewise(VectorOperators.Binary,long,VectorMask) */ public abstract Vector<E> lanewise(VectorOperators.Binary op, long e); /** * Combines the corresponding lane values of this vector * with those of a second input vector, * with selection of lane elements controlled by a mask. * * This is a lane-wise binary operation which applies * the selected operation to each lane. * The second operand is a broadcast integral value. * The return value will be equal to this expression: * {@code this.lanewise(op, this.broadcast(e), m)}. * * @apiNote * The {@code long} value {@code e} must be accurately * representable by the {@code ETYPE} of this vector's species, * so that {@code e==(long)(ETYPE)e}. This rule is enforced * by the implicit call to {@code broadcast()}. * <p> * Subtypes improve on this method by sharpening * the method return type and * the type of the scalar parameter {@code e}. * * @param op the operation used to combine lane values * @param e the input scalar * @param m the mask controlling lane selection * @return the result of applying the operation lane-wise * to the input vector and the scalar * @throws UnsupportedOperationException if this vector does * not support the requested operation * @throws IllegalArgumentException * if the given {@code long} value cannot * be represented by the right operand type * of the vector operation * @see #broadcast(long) * @see #lanewise(VectorOperators.Binary,Vector,VectorMask) */ public abstract Vector<E> lanewise(VectorOperators.Binary op, long e, VectorMask<E> m); /** * Combines the corresponding lane values of this vector * with the lanes of a second and a third input vector. * * This is a <a href="Vector.html#lane-wise">lane-wise</a> * ternary operation which applies * the selected operation to each lane. * * @apiNote * Subtypes improve on this method by sharpening * the method return type. * * @param op the operation used to combine lane values * @param v1 the second input vector * @param v2 the third input vector * @return the result of applying the operation lane-wise * to the three input vectors * @throws UnsupportedOperationException if this vector does * not support the requested operation * @see VectorOperators#BITWISE_BLEND * @see VectorOperators#FMA * @see #lanewise(VectorOperators.Unary) * @see #lanewise(VectorOperators.Binary,Vector) * @see #lanewise(VectorOperators.Ternary,Vector,Vector,VectorMask) */ public abstract Vector<E> lanewise(VectorOperators.Ternary op, Vector<E> v1, Vector<E> v2); /** * Combines the corresponding lane values of this vector * with the lanes of a second and a third input vector, * with selection of lane elements controlled by a mask. * * This is a lane-wise ternary operation which applies * the selected operation to each lane. * * @apiNote * Subtypes improve on this method by sharpening * the method return type. * * @param op the operation used to combine lane values * @param v1 the second input vector * @param v2 the third input vector * @param m the mask controlling lane selection * @return the result of applying the operation lane-wise * to the three input vectors * @throws UnsupportedOperationException if this vector does * not support the requested operation * @see #lanewise(VectorOperators.Ternary,Vector,Vector) */ public abstract Vector<E> lanewise(VectorOperators.Ternary op, Vector<E> v1, Vector<E> v2, VectorMask<E> m); // Note: lanewise(Binary) has two rudimentary broadcast // operations from an approximate scalar type (long). // We do both with that, here, for lanewise(Ternary). // The vector subtypes supply a full suite of // broadcasting and masked lanewise operations // for their specific ETYPEs: // lanewise(Unary, [mask]) // lanewise(Binary, [e | v], [mask]) // lanewise(Ternary, [e1 | v1], [e2 | v2], [mask]) /// Full-service binary ops: ADD, SUB, MUL, DIV // Full-service functions support all four variations // of vector vs. broadcast scalar, and mask vs. not. // The lanewise generic operator is (by this definition) // also a full-service function. // Other named functions handle just the one named // variation. Most lanewise operations are *not* named, // and are reached only by lanewise. /** * Adds this vector to a second input vector. * * This is a lane-wise binary operation which applies * the primitive addition operation ({@code +}) * to each pair of corresponding lane values. * * This method is also equivalent to the expression * {@link #lanewise(VectorOperators.Binary,Vector) * lanewise}{@code (}{@link VectorOperators#ADD * ADD}{@code , v)}. * * <p> * As a full-service named operation, this method * comes in masked and unmasked overloadings, and * (in subclasses) also comes in scalar-broadcast * overloadings (both masked and unmasked). * * @param v a second input vector * @return the result of adding this vector to the second input vector * @see #add(Vector,VectorMask) * @see IntVector#add(int) * @see VectorOperators#ADD * @see #lanewise(VectorOperators.Binary,Vector) * @see IntVector#lanewise(VectorOperators.Binary,int) */ public abstract Vector<E> add(Vector<E> v); /** * Adds this vector to a second input vector, selecting lanes * under the control of a mask. * * This is a masked lane-wise binary operation which applies * the primitive addition operation ({@code +}) * to each pair of corresponding lane values. * * For any lane unset in the mask, the primitive operation is * suppressed and this vector retains the original value stored in * that lane. * * This method is also equivalent to the expression * {@link #lanewise(VectorOperators.Binary,Vector,VectorMask) * lanewise}{@code (}{@link VectorOperators#ADD * ADD}{@code , v, m)}. * * <p> * As a full-service named operation, this method * comes in masked and unmasked overloadings, and * (in subclasses) also comes in scalar-broadcast * overloadings (both masked and unmasked). * * @param v the second input vector * @param m the mask controlling lane selection * @return the result of adding this vector to the given vector * @see #add(Vector) * @see IntVector#add(int,VectorMask) * @see VectorOperators#ADD * @see #lanewise(VectorOperators.Binary,Vector,VectorMask) * @see IntVector#lanewise(VectorOperators.Binary,int,VectorMask) */ public abstract Vector<E> add(Vector<E> v, VectorMask<E> m); /** * Subtracts a second input vector from this vector. * * This is a lane-wise binary operation which applies * the primitive subtraction operation ({@code -}) * to each pair of corresponding lane values. * * This method is also equivalent to the expression * {@link #lanewise(VectorOperators.Binary,Vector) * lanewise}{@code (}{@link VectorOperators#SUB * SUB}{@code , v)}. * * <p> * As a full-service named operation, this method * comes in masked and unmasked overloadings, and * (in subclasses) also comes in scalar-broadcast * overloadings (both masked and unmasked). * * @param v a second input vector * @return the result of subtracting the second input vector from this vector * @see #sub(Vector,VectorMask) * @see IntVector#sub(int) * @see VectorOperators#SUB * @see #lanewise(VectorOperators.Binary,Vector) * @see IntVector#lanewise(VectorOperators.Binary,int) */ public abstract Vector<E> sub(Vector<E> v); /** * Subtracts a second input vector from this vector * under the control of a mask. * * This is a masked lane-wise binary operation which applies * the primitive subtraction operation ({@code -}) * to each pair of corresponding lane values. * * For any lane unset in the mask, the primitive operation is * suppressed and this vector retains the original value stored in * that lane. * * This method is also equivalent to the expression * {@link #lanewise(VectorOperators.Binary,Vector,VectorMask) * lanewise}{@code (}{@link VectorOperators#SUB * SUB}{@code , v, m)}. * * <p> * As a full-service named operation, this method * comes in masked and unmasked overloadings, and * (in subclasses) also comes in scalar-broadcast * overloadings (both masked and unmasked). * * @param v the second input vector * @param m the mask controlling lane selection * @return the result of subtracting the second input vector from this vector * @see #sub(Vector) * @see IntVector#sub(int,VectorMask) * @see VectorOperators#SUB * @see #lanewise(VectorOperators.Binary,Vector,VectorMask) * @see IntVector#lanewise(VectorOperators.Binary,int,VectorMask) */ public abstract Vector<E> sub(Vector<E> v, VectorMask<E> m); /** * Multiplies this vector by a second input vector. * * This is a lane-wise binary operation which applies * the primitive multiplication operation ({@code *}) * to each pair of corresponding lane values. * * This method is also equivalent to the expression * {@link #lanewise(VectorOperators.Binary,Vector) * lanewise}{@code (}{@link VectorOperators#MUL * MUL}{@code , v)}. * * <p> * As a full-service named operation, this method * comes in masked and unmasked overloadings, and * (in subclasses) also comes in scalar-broadcast * overloadings (both masked and unmasked). * * @param v a second input vector * @return the result of multiplying this vector by the second input vector * @see #mul(Vector,VectorMask) * @see IntVector#mul(int) * @see VectorOperators#MUL * @see #lanewise(VectorOperators.Binary,Vector) * @see IntVector#lanewise(VectorOperators.Binary,int) */ public abstract Vector<E> mul(Vector<E> v); /** * Multiplies this vector by a second input vector * under the control of a mask. * * This is a lane-wise binary operation which applies * the primitive multiplication operation ({@code *}) * to each pair of corresponding lane values. * * For any lane unset in the mask, the primitive operation is * suppressed and this vector retains the original value stored in * that lane. * * This method is also equivalent to the expression * {@link #lanewise(VectorOperators.Binary,Vector,VectorMask) * lanewise}{@code (}{@link VectorOperators#MUL * MUL}{@code , v, m)}. * * <p> * As a full-service named operation, this method * comes in masked and unmasked overloadings, and * (in subclasses) also comes in scalar-broadcast * overloadings (both masked and unmasked). * * @param v the second input vector * @param m the mask controlling lane selection * @return the result of multiplying this vector by the given vector * @see #mul(Vector) * @see IntVector#mul(int,VectorMask) * @see VectorOperators#MUL * @see #lanewise(VectorOperators.Binary,Vector,VectorMask) * @see IntVector#lanewise(VectorOperators.Binary,int,VectorMask) */ public abstract Vector<E> mul(Vector<E> v, VectorMask<E> m); /** * Divides this vector by a second input vector. * * This is a lane-wise binary operation which applies * the primitive division operation ({@code /}) * to each pair of corresponding lane values. * * This method is also equivalent to the expression * {@link #lanewise(VectorOperators.Binary,Vector) * lanewise}{@code (}{@link VectorOperators#DIV * DIV}{@code , v)}. * * <p> * As a full-service named operation, this method * comes in masked and unmasked overloadings, and * (in subclasses) also comes in scalar-broadcast * overloadings (both masked and unmasked). * * @apiNote If the underlying scalar operator does not support * division by zero, but is presented with a zero divisor, * an {@code ArithmeticException} will be thrown. * * @param v a second input vector * @return the result of dividing this vector by the second input vector * @throws ArithmeticException if any lane * in {@code v} is zero * and {@code ETYPE} is not {@code float} or {@code double}. * @see #div(Vector,VectorMask) * @see DoubleVector#div(double) * @see VectorOperators#DIV * @see #lanewise(VectorOperators.Binary,Vector) * @see IntVector#lanewise(VectorOperators.Binary,int) */ public abstract Vector<E> div(Vector<E> v); /** * Divides this vector by a second input vector * under the control of a mask. * * This is a lane-wise binary operation which applies * the primitive division operation ({@code /}) * to each pair of corresponding lane values. * * For any lane unset in the mask, the primitive operation is * suppressed and this vector retains the original value stored in * that lane. * * This method is also equivalent to the expression * {@link #lanewise(VectorOperators.Binary,Vector,VectorMask) * lanewise}{@code (}{@link VectorOperators#DIV * DIV}{@code , v, m)}. * * <p> * As a full-service named operation, this method * comes in masked and unmasked overloadings, and * (in subclasses) also comes in scalar-broadcast * overloadings (both masked and unmasked). * * @apiNote If the underlying scalar operator does not support * division by zero, but is presented with a zero divisor, * an {@code ArithmeticException} will be thrown. * * @param v a second input vector * @param m the mask controlling lane selection * @return the result of dividing this vector by the second input vector * @throws ArithmeticException if any lane selected by {@code m} * in {@code v} is zero * and {@code ETYPE} is not {@code float} or {@code double}. * @see #div(Vector) * @see DoubleVector#div(double,VectorMask) * @see VectorOperators#DIV * @see #lanewise(VectorOperators.Binary,Vector,VectorMask) * @see DoubleVector#lanewise(VectorOperators.Binary,double,VectorMask) */ public abstract Vector<E> div(Vector<E> v, VectorMask<E> m); /// END OF FULL-SERVICE BINARY METHODS /// Non-full-service unary ops: NEG, ABS /** * Negates this vector. * * This is a lane-wise unary operation which applies * the primitive negation operation ({@code -x}) * to each input lane. * * This method is also equivalent to the expression * {@link #lanewise(VectorOperators.Unary) * lanewise}{@code (}{@link VectorOperators#NEG * NEG}{@code )}. * * @apiNote * This method has no masked variant, but the corresponding * masked operation can be obtained from the * {@linkplain #lanewise(VectorOperators.Unary,VectorMask) * lanewise method}. * * @return the negation of this vector * @see VectorOperators#NEG * @see #lanewise(VectorOperators.Unary) * @see #lanewise(VectorOperators.Unary,VectorMask) */ public abstract Vector<E> neg(); /** * Returns the absolute value of this vector. * * This is a lane-wise unary operation which applies * the method {@code Math.abs} * to each input lane. * * This method is also equivalent to the expression * {@link #lanewise(VectorOperators.Unary) * lanewise}{@code (}{@link VectorOperators#ABS * ABS}{@code )}. * * @apiNote * This method has no masked variant, but the corresponding * masked operation can be obtained from the * {@linkplain #lanewise(VectorOperators.Unary,VectorMask) * lanewise method}. * * @return the absolute value of this vector * @see VectorOperators#ABS * @see #lanewise(VectorOperators.Unary) * @see #lanewise(VectorOperators.Unary,VectorMask) */ public abstract Vector<E> abs(); /// Non-full-service binary ops: MIN, MAX /** * Computes the smaller of this vector and a second input vector. * * This is a lane-wise binary operation which applies the * operation {@code Math.min()} to each pair of * corresponding lane values. * * This method is also equivalent to the expression * {@link #lanewise(VectorOperators.Binary,Vector) * lanewise}{@code (}{@link VectorOperators#MIN * MIN}{@code , v)}. * * @apiNote * This is not a full-service named operation like * {@link #add(Vector) add()}. A masked version of * this operation is not directly available * but may be obtained via the masked version of * {@code lanewise}. Subclasses define an additional * scalar-broadcast overloading of this method. * * @param v a second input vector * @return the lanewise minimum of this vector and the second input vector * @see IntVector#min(int) * @see VectorOperators#MIN * @see #lanewise(VectorOperators.Binary,Vector) * @see #lanewise(VectorOperators.Binary,Vector,VectorMask) */ public abstract Vector<E> min(Vector<E> v); /** * Computes the larger of this vector and a second input vector. * * This is a lane-wise binary operation which applies the * operation {@code Math.max()} to each pair of * corresponding lane values. * * This method is also equivalent to the expression * {@link #lanewise(VectorOperators.Binary,Vector) * lanewise}{@code (}{@link VectorOperators#MAX * MAX}{@code , v)}. * * <p> * This is not a full-service named operation like * {@link #add(Vector) add()}. A masked version of * this operation is not directly available * but may be obtained via the masked version of * {@code lanewise}. Subclasses define an additional * scalar-broadcast overloading of this method. * * @param v a second input vector * @return the lanewise maximum of this vector and the second input vector * @see IntVector#max(int) * @see VectorOperators#MAX * @see #lanewise(VectorOperators.Binary,Vector) * @see #lanewise(VectorOperators.Binary,Vector,VectorMask) */ public abstract Vector<E> max(Vector<E> v); // Reductions /** * Returns a value accumulated from all the lanes of this vector. * * This is an associative cross-lane reduction operation which * applies the specified operation to all the lane elements. * The return value will be equal to this expression: * {@code (long) ((EVector)this).reduceLanes(op)}, where {@code EVector} * is the vector class specific to this vector's element type * {@code ETYPE}. * <p> * In the case of operations {@code ADD} and {@code MUL}, * when {@code ETYPE} is {@code float} or {@code double}, * the precise result, before casting, will reflect the choice * of an arbitrary order of operations, which may even vary over time. * For further details see the section * <a href="VectorOperators.html#fp_assoc">Operations on floating point vectors</a>. * * @apiNote * If the {@code ETYPE} is {@code float} or {@code double}, * this operation can lose precision and/or range, as a * normal part of casting the result down to {@code long}. * * Usually * {@linkplain IntVector#reduceLanes(VectorOperators.Associative) * strongly typed access} * is preferable, if you are working with a vector * subtype that has a known element type. * * @param op the operation used to combine lane values * @return the accumulated result, cast to {@code long} * @throws UnsupportedOperationException if this vector does * not support the requested operation * @see #reduceLanesToLong(VectorOperators.Associative,VectorMask) * @see IntVector#reduceLanes(VectorOperators.Associative) * @see FloatVector#reduceLanes(VectorOperators.Associative) */ public abstract long reduceLanesToLong(VectorOperators.Associative op); /** * Returns a value accumulated from selected lanes of this vector, * controlled by a mask. * * This is an associative cross-lane reduction operation which * applies the specified operation to the selected lane elements. * The return value will be equal to this expression: * {@code (long) ((EVector)this).reduceLanes(op, m)}, where {@code EVector} * is the vector class specific to this vector's element type * {@code ETYPE}. * <p> * If no elements are selected, an operation-specific identity * value is returned. * <ul> * <li> * If the operation is {@code ADD}, {@code XOR}, or {@code OR}, * then the identity value is zero. * <li> * If the operation is {@code MUL}, * then the identity value is one. * <li> * If the operation is {@code AND}, * then the identity value is minus one (all bits set). * <li> * If the operation is {@code MAX}, * then the identity value is the {@code MIN_VALUE} * of the vector's native {@code ETYPE}. * (In the case of floating point types, the value * {@code NEGATIVE_INFINITY} is used, and will appear * after casting as {@code Long.MIN_VALUE}. * <li> * If the operation is {@code MIN}, * then the identity value is the {@code MAX_VALUE} * of the vector's native {@code ETYPE}. * (In the case of floating point types, the value * {@code POSITIVE_INFINITY} is used, and will appear * after casting as {@code Long.MAX_VALUE}. * </ul> * <p> * In the case of operations {@code ADD} and {@code MUL}, * when {@code ETYPE} is {@code float} or {@code double}, * the precise result, before casting, will reflect the choice * of an arbitrary order of operations, which may even vary over time. * For further details see the section * <a href="VectorOperators.html#fp_assoc">Operations on floating point vectors</a>. * * @apiNote * If the {@code ETYPE} is {@code float} or {@code double}, * this operation can lose precision and/or range, as a * normal part of casting the result down to {@code long}. * * Usually * {@linkplain IntVector#reduceLanes(VectorOperators.Associative,VectorMask) * strongly typed access} * is preferable, if you are working with a vector * subtype that has a known element type. * * @param op the operation used to combine lane values * @param m the mask controlling lane selection * @return the reduced result accumulated from the selected lane values * @throws UnsupportedOperationException if this vector does * not support the requested operation * @see #reduceLanesToLong(VectorOperators.Associative) * @see IntVector#reduceLanes(VectorOperators.Associative,VectorMask) * @see FloatVector#reduceLanes(VectorOperators.Associative,VectorMask) */ public abstract long reduceLanesToLong(VectorOperators.Associative op, VectorMask<E> m); // Lanewise unary tests /** * Tests the lanes of this vector * according to the given operation. * * This is a lane-wise unary test operation which applies * the given test operation * to each lane value. * @param op the operation used to test lane values * @return the mask result of testing the lanes of this vector, * according to the selected test operator * @see VectorOperators.Comparison * @see #test(VectorOperators.Test, VectorMask) * @see #compare(VectorOperators.Comparison, Vector) */ public abstract VectorMask<E> test(VectorOperators.Test op); /** * Test selected lanes of this vector, * according to the given operation. * * This is a masked lane-wise unary test operation which applies * the given test operation * to each lane value. * * The returned result is equal to the expression * {@code test(op).and(m)}. * * @param op the operation used to test lane values * @param m the mask controlling lane selection * @return the mask result of testing the lanes of this vector, * according to the selected test operator, * and only in the lanes selected by the mask * @see #test(VectorOperators.Test) */ public abstract VectorMask<E> test(VectorOperators.Test op, VectorMask<E> m); // Comparisons /** * Tests if this vector is equal to another input vector. * * This is a lane-wise binary test operation which applies * the primitive equals operation ({@code ==}) * to each pair of corresponding lane values. * The result is the same as {@code compare(VectorOperators.EQ, v)}. * * @param v a second input vector * @return the mask result of testing lane-wise if this vector * equal to the second input vector * @see #compare(VectorOperators.Comparison,Vector) * @see VectorOperators#EQ * @see #equals */ public abstract VectorMask<E> eq(Vector<E> v); /** * Tests if this vector is less than another input vector. * * This is a lane-wise binary test operation which applies * the primitive less-than operation ({@code <}) to each lane. * The result is the same as {@code compare(VectorOperators.LT, v)}. * * @param v a second input vector * @return the mask result of testing lane-wise if this vector * is less than the second input vector * @see #compare(VectorOperators.Comparison,Vector) * @see VectorOperators#LT */ public abstract VectorMask<E> lt(Vector<E> v); /** * Tests this vector by comparing it with another input vector, * according to the given comparison operation. * * This is a lane-wise binary test operation which applies * the given comparison operation * to each pair of corresponding lane values. * * @param op the operation used to compare lane values * @param v a second input vector * @return the mask result of testing lane-wise if this vector * compares to the input, according to the selected * comparison operator * @see #eq(Vector) * @see #lt(Vector) * @see VectorOperators.Comparison * @see #compare(VectorOperators.Comparison, Vector, VectorMask) * @see #test(VectorOperators.Test) */ public abstract VectorMask<E> compare(VectorOperators.Comparison op, Vector<E> v); /** * Tests this vector by comparing it with another input vector, * according to the given comparison operation, * in lanes selected by a mask. * * This is a masked lane-wise binary test operation which applies * the given comparison operation * to each pair of corresponding lane values. * * The returned result is equal to the expression * {@code compare(op,v).and(m)}. * * @param op the operation used to compare lane values * @param v a second input vector * @param m the mask controlling lane selection * @return the mask result of testing lane-wise if this vector * compares to the input, according to the selected * comparison operator, * and only in the lanes selected by the mask * @see #compare(VectorOperators.Comparison, Vector) */ public abstract VectorMask<E> compare(VectorOperators.Comparison op, Vector<E> v, VectorMask<E> m); /** * Tests this vector by comparing it with an input scalar, * according to the given comparison operation. * * This is a lane-wise binary test operation which applies * the given comparison operation * to each lane value, paired with the broadcast value. * * <p> * The result is the same as * {@code this.compare(op, this.broadcast(e))}. * That is, the scalar may be regarded as broadcast to * a vector of the same species, and then compared * against the original vector, using the selected * comparison operation. * * @apiNote * The {@code long} value {@code e} must be accurately * representable by the {@code ETYPE} of this vector's species, * so that {@code e==(long)(ETYPE)e}. This rule is enforced * by the implicit call to {@code broadcast()}. * <p> * Subtypes improve on this method by sharpening * the type of the scalar parameter {@code e}. * * @param op the operation used to compare lane values * @param e the input scalar * @return the mask result of testing lane-wise if this vector * compares to the input, according to the selected * comparison operator * @throws IllegalArgumentException * if the given {@code long} value cannot * be represented by the vector's {@code ETYPE} * @see #broadcast(long) * @see #compare(VectorOperators.Comparison,Vector) */ public abstract VectorMask<E> compare(VectorOperators.Comparison op, long e); /** * Tests this vector by comparing it with an input scalar, * according to the given comparison operation, * in lanes selected by a mask. * * This is a masked lane-wise binary test operation which applies * the given comparison operation * to each lane value, paired with the broadcast value. * * The returned result is equal to the expression * {@code compare(op,e).and(m)}. * * @apiNote * The {@code long} value {@code e} must be accurately * representable by the {@code ETYPE} of this vector's species, * so that {@code e==(long)(ETYPE)e}. This rule is enforced * by the implicit call to {@code broadcast()}. * <p> * Subtypes improve on this method by sharpening * the type of the scalar parameter {@code e}. * * @param op the operation used to compare lane values * @param e the input scalar * @param m the mask controlling lane selection * @return the mask result of testing lane-wise if this vector * compares to the input, according to the selected * comparison operator, * and only in the lanes selected by the mask * @throws IllegalArgumentException * if the given {@code long} value cannot * be represented by the vector's {@code ETYPE} * @see #broadcast(long) * @see #compare(VectorOperators.Comparison,Vector) */ public abstract VectorMask<E> compare(VectorOperators.Comparison op, long e, VectorMask<E> m); /** * Replaces selected lanes of this vector with * corresponding lanes from a second input vector * under the control of a mask. * * This is a masked lane-wise binary operation which * selects each lane value from one or the other input. * * <ul> * <li> * For any lane <em>set</em> in the mask, the new lane value * is taken from the second input vector, and replaces * whatever value was in the that lane of this vector. * <li> * For any lane <em>unset</em> in the mask, the replacement is * suppressed and this vector retains the original value stored in * that lane. * </ul> * * The following pseudocode illustrates this behavior: * <pre>{@code * Vector<E> a = ...; * VectorSpecies<E> species = a.species(); * Vector<E> b = ...; * b.check(species); * VectorMask<E> m = ...; * ETYPE[] ar = a.toArray(); * for (int i = 0; i < ar.length; i++) { * if (m.laneIsSet(i)) { * ar[i] = b.lane(i); * } * } * return EVector.fromArray(s, ar, 0); * }</pre> * * @param v the second input vector, containing replacement lane values * @param m the mask controlling lane selection from the second input vector * @return the result of blending the lane elements of this vector with * those of the second input vector */ public abstract Vector<E> blend(Vector<E> v, VectorMask<E> m); /** * Replaces selected lanes of this vector with * a scalar value * under the control of a mask. * * This is a masked lane-wise binary operation which * selects each lane value from one or the other input. * * The returned result is equal to the expression * {@code blend(broadcast(e),m)}. * * @apiNote * The {@code long} value {@code e} must be accurately * representable by the {@code ETYPE} of this vector's species, * so that {@code e==(long)(ETYPE)e}. This rule is enforced * by the implicit call to {@code broadcast()}. * <p> * Subtypes improve on this method by sharpening * the type of the scalar parameter {@code e}. * * @param e the input scalar, containing the replacement lane value * @param m the mask controlling lane selection of the scalar * @return the result of blending the lane elements of this vector with * the scalar value */ public abstract Vector<E> blend(long e, VectorMask<E> m); /** * Adds the lanes of this vector to their corresponding * lane numbers, scaled by a given constant. * * This is a lane-wise unary operation which, for * each lane {@code N}, computes the scaled index value * {@code N*scale} and adds it to the value already * in lane {@code N} of the current vector. * * <p> The scale must not be so large, and the element size must * not be so small, that that there would be an overflow when * computing any of the {@code N*scale} or {@code VLENGTH*scale}, * when the the result is represented using the vector * lane type {@code ETYPE}. * * <p> * The following pseudocode illustrates this behavior: * <pre>{@code * Vector<E> a = ...; * VectorSpecies<E> species = a.species(); * ETYPE[] ar = a.toArray(); * for (int i = 0; i < ar.length; i++) { * long d = (long)i * scale; * if (d != (ETYPE) d) throw ...; * ar[i] += (ETYPE) d; * } * long d = (long)ar.length * scale; * if (d != (ETYPE) d) throw ...; * return EVector.fromArray(s, ar, 0); * }</pre> * * @param scale the number to multiply by each lane index * {@code N}, typically {@code 1} * @return the result of incrementing each lane element by its * corresponding lane index {@code N}, scaled by {@code scale} * @throws IllegalArgumentException * if the values in the interval * {@code [0..VLENGTH*scale]} * are not representable by the {@code ETYPE} */ public abstract Vector<E> addIndex(int scale); // Slicing segments of adjacent lanes /** * Slices a segment of adjacent lanes, starting at a given * {@code origin} lane in the current vector, and continuing (as * needed) into an immediately following vector. The block of * {@code VLENGTH} lanes is extracted into its own vector and * returned. * * <p> This is a cross-lane operation that shifts lane elements * to the front, from the current vector and the second vector. * Both vectors can be viewed as a combined "background" of length * {@code 2*VLENGTH}, from which a slice is extracted. * * The lane numbered {@code N} in the output vector is copied * from lane {@code origin+N} of the input vector, if that * lane exists, else from lane {@code origin+N-VLENGTH} of * the second vector (which is guaranteed to exist). * * <p> The {@code origin} value must be in the inclusive range * {@code 0..VLENGTH}. As limiting cases, {@code v.slice(0,w)} * and {@code v.slice(VLENGTH,w)} return {@code v} and {@code w}, * respectively. * * @apiNote * * This method may be regarded as the inverse of * {@link #unslice(int,Vector,int) unslice()}, * in that the sliced value could be unsliced back into its * original position in the two input vectors, without * disturbing unrelated elements, as in the following * pseudocode: * <pre>{@code * EVector slice = v1.slice(origin, v2); * EVector w1 = slice.unslice(origin, v1, 0); * EVector w2 = slice.unslice(origin, v2, 1); * assert v1.equals(w1); * assert v2.equals(w2); * }</pre> * * <p> This method also supports a variety of cross-lane shifts and * rotates as follows: * <ul> * * <li>To shift lanes forward to the front of the vector, supply a * zero vector for the second operand and specify the shift count * as the origin. For example: {@code v.slice(shift, v.broadcast(0))}. * * <li>To shift lanes backward to the back of the vector, supply a * zero vector for the <em>first</em> operand, and specify the * negative shift count as the origin (modulo {@code VLENGTH}. * For example: {@code v.broadcast(0).slice(v.length()-shift, v)}. * * <li>To rotate lanes forward toward the front end of the vector, * cycling the earliest lanes around to the back, supply the same * vector for both operands and specify the rotate count as the * origin. For example: {@code v.slice(rotate, v)}. * * <li>To rotate lanes backward toward the back end of the vector, * cycling the latest lanes around to the front, supply the same * vector for both operands and specify the negative of the rotate * count (modulo {@code VLENGTH}) as the origin. For example: * {@code v.slice(v.length() - rotate, v)}. * * <li> * Since {@code origin} values less then zero or more than * {@code VLENGTH} will be rejected, if you need to rotate * by an unpredictable multiple of {@code VLENGTH}, be sure * to reduce the origin value into the required range. * The {@link VectorSpecies#loopBound(int) loopBound()} * method can help with this. For example: * {@code v.slice(rotate - v.species().loopBound(rotate), v)}. * * </ul> * * @param origin the first input lane to transfer into the slice * @param v1 a second vector logically concatenated with the first, * before the slice is taken (if omitted it defaults to zero) * @return a contiguous slice of {@code VLENGTH} lanes, taken from * this vector starting at the indicated origin, and * continuing (as needed) into the second vector * @throws ArrayIndexOutOfBoundsException if {@code origin} * is negative or greater than {@code VLENGTH} * @see #slice(int,Vector,VectorMask) * @see #slice(int) * @see #unslice(int,Vector,int) */ public abstract Vector<E> slice(int origin, Vector<E> v1); /** * Slices a segment of adjacent lanes * under the control of a mask, * starting at a given * {@code origin} lane in the current vector, and continuing (as * needed) into an immediately following vector. The block of * {@code VLENGTH} lanes is extracted into its own vector and * returned. * * The resulting vector will be zero in all lanes unset in the * given mask. Lanes set in the mask will contain data copied * from selected lanes of {@code this} or {@code v1}. * * <p> This is a cross-lane operation that shifts lane elements * to the front, from the current vector and the second vector. * Both vectors can be viewed as a combined "background" of length * {@code 2*VLENGTH}, from which a slice is extracted. * * The returned result is equal to the expression * {@code broadcast(0).blend(slice(origin,v1),m)}. * * @apiNote * This method may be regarded as the inverse of * {@code #unslice(int,Vector,int,VectorMask) unslice()}, * in that the sliced value could be unsliced back into its * original position in the two input vectors, without * disturbing unrelated elements, as in the following * pseudocode: * <pre>{@code * EVector slice = v1.slice(origin, v2, m); * EVector w1 = slice.unslice(origin, v1, 0, m); * EVector w2 = slice.unslice(origin, v2, 1, m); * assert v1.equals(w1); * assert v2.equals(w2); * }</pre> * * @param origin the first input lane to transfer into the slice * @param v1 a second vector logically concatenated with the first, * before the slice is taken (if omitted it defaults to zero) * @param m the mask controlling lane selection into the resulting vector * @return a contiguous slice of {@code VLENGTH} lanes, taken from * this vector starting at the indicated origin, and * continuing (as needed) into the second vector * @throws ArrayIndexOutOfBoundsException if {@code origin} * is negative or greater than {@code VLENGTH} * @see #slice(int,Vector) * @see #unslice(int,Vector,int,VectorMask) */ // This doesn't pull its weight, but its symmetrical with // masked unslice, and might cause questions if missing. // It could make for clearer code. public abstract Vector<E> slice(int origin, Vector<E> v1, VectorMask<E> m); /** * Slices a segment of adjacent lanes, starting at a given * {@code origin} lane in the current vector. A block of * {@code VLENGTH} lanes, possibly padded with zero lanes, is * extracted into its own vector and returned. * * This is a convenience method which slices from a single * vector against an extended background of zero lanes. * It is equivalent to * {@link #slice(int,Vector) slice}{@code * (origin, }{@link #broadcast(long) broadcast}{@code (0))}. * It may also be viewed simply as a cross-lane shift * from later to earlier lanes, with zeroes filling * in the vacated lanes at the end of the vector. * In this view, the shift count is {@code origin}. * * @param origin the first input lane to transfer into the slice * @return the last {@code VLENGTH-origin} input lanes, * placed starting in the first lane of the ouput, * padded at the end with zeroes * @throws ArrayIndexOutOfBoundsException if {@code origin} * is negative or greater than {@code VLENGTH} * @see #slice(int,Vector) * @see #unslice(int,Vector,int) */ // This API point pulls its weight as a teaching aid, // though it's a one-off and broadcast(0) is easy. public abstract Vector<E> slice(int origin); /** * Reverses a {@linkplain #slice(int,Vector) slice()}, inserting * the current vector as a slice within another "background" input * vector, which is regarded as one or the other input to a * hypothetical subsequent {@code slice()} operation. * * <p> This is a cross-lane operation that permutes the lane * elements of the current vector toward the back and inserts them * into a logical pair of background vectors. Only one of the * pair will be returned, however. The background is formed by * duplicating the second input vector. (However, the output will * never contain two duplicates from the same input lane.) * * The lane numbered {@code N} in the input vector is copied into * lane {@code origin+N} of the first background vector, if that * lane exists, else into lane {@code origin+N-VLENGTH} of the * second background vector (which is guaranteed to exist). * * The first or second background vector, updated with the * inserted slice, is returned. The {@code part} number of zero * or one selects the first or second updated background vector. * * <p> The {@code origin} value must be in the inclusive range * {@code 0..VLENGTH}. As limiting cases, {@code v.unslice(0,w,0)} * and {@code v.unslice(VLENGTH,w,1)} both return {@code v}, while * {@code v.unslice(0,w,1)} and {@code v.unslice(VLENGTH,w,0)} * both return {@code w}. * * @apiNote * This method supports a variety of cross-lane insertion * operations as follows: * <ul> * * <li>To insert near the end of a background vector {@code w} * at some offset, specify the offset as the origin and * select part zero. For example: {@code v.unslice(offset, w, 0)}. * * <li>To insert near the end of a background vector {@code w}, * but capturing the overflow into the next vector {@code x}, * specify the offset as the origin and select part one. * For example: {@code v.unslice(offset, x, 1)}. * * <li>To insert the last {@code N} items near the beginning * of a background vector {@code w}, supply a {@code VLENGTH-N} * as the origin and select part one. * For example: {@code v.unslice(v.length()-N, w)}. * * </ul> * * @param origin the first output lane to receive the slice * @param w the background vector that (as two copies) will receive * the inserted slice * @param part the part number of the result (either zero or one) * @return either the first or second part of a pair of * background vectors {@code w}, updated by inserting * this vector at the indicated origin * @throws ArrayIndexOutOfBoundsException if {@code origin} * is negative or greater than {@code VLENGTH}, * or if {@code part} is not zero or one * @see #slice(int,Vector) * @see #unslice(int,Vector,int,VectorMask) */ public abstract Vector<E> unslice(int origin, Vector<E> w, int part); /** * Reverses a {@linkplain #slice(int,Vector) slice()}, inserting * (under the control of a mask) * the current vector as a slice within another "background" input * vector, which is regarded as one or the other input to a * hypothetical subsequent {@code slice()} operation. * * <p> This is a cross-lane operation that permutes the lane * elements of the current vector forward and inserts its lanes * (when selected by the mask) into a logical pair of background * vectors. As with the * {@linkplain #unslice(int,Vector,int) unmasked version} of this method, * only one of the pair will be returned, as selected by the * {@code part} number. * * For each lane {@code N} selected by the mask, the lane value * is copied into * lane {@code origin+N} of the first background vector, if that * lane exists, else into lane {@code origin+N-VLENGTH} of the * second background vector (which is guaranteed to exist). * Background lanes retain their original values if the * corresponding input lanes {@code N} are unset in the mask. * * The first or second background vector, updated with set lanes * of the inserted slice, is returned. The {@code part} number of * zero or one selects the first or second updated background * vector. * * @param origin the first output lane to receive the slice * @param w the background vector that (as two copies) will receive * the inserted slice, if they are set in {@code m} * @param part the part number of the result (either zero or one) * @param m the mask controlling lane selection from the current vector * @return either the first or second part of a pair of * background vectors {@code w}, updated by inserting * selected lanes of this vector at the indicated origin * @throws ArrayIndexOutOfBoundsException if {@code origin} * is negative or greater than {@code VLENGTH}, * or if {@code part} is not zero or one * @see #unslice(int,Vector,int) * @see #slice(int,Vector) */ public abstract Vector<E> unslice(int origin, Vector<E> w, int part, VectorMask<E> m); /** * Reverses a {@linkplain #slice(int) slice()}, inserting * the current vector as a slice within a "background" input * of zero lane values. Compared to other {@code unslice()} * methods, this method only returns the first of the * pair of background vectors. * * This is a convenience method which returns the result of * {@link #unslice(int,Vector,int) unslice}{@code * (origin, }{@link #broadcast(long) broadcast}{@code (0), 0)}. * It may also be viewed simply as a cross-lane shift * from earlier to later lanes, with zeroes filling * in the vacated lanes at the beginning of the vector. * In this view, the shift count is {@code origin}. * * @param origin the first output lane to receive the slice * @return the first {@code VLENGTH-origin} input lanes, * placed starting at the given origin, * padded at the beginning with zeroes * @throws ArrayIndexOutOfBoundsException if {@code origin} * is negative or greater than {@code VLENGTH} * @see #unslice(int,Vector,int) * @see #slice(int) */ // This API point pulls its weight as a teaching aid, // though it's a one-off and broadcast(0) is easy. public abstract Vector<E> unslice(int origin); // ISSUE: Add a slice which uses a mask instead of an origin? //public abstract Vector<E> slice(VectorMask<E> support); // ISSUE: Add some more options for questionable edge conditions? // We might define enum EdgeOption { ERROR, ZERO, WRAP } for the // default of throwing AIOOBE, or substituting zeroes, or just // reducing the out-of-bounds index modulo VLENGTH. Similar // concerns also apply to general Shuffle operations. For now, // just support ERROR, since that is safest. /** * Rearranges the lane elements of this vector, selecting lanes * under the control of a specific shuffle. * * This is a cross-lane operation that rearranges the lane * elements of this vector. * * For each lane {@code N} of the shuffle, and for each lane * source index {@code I=s.laneSource(N)} in the shuffle, * the output lane {@code N} obtains the value from * the input vector at lane {@code I}. * * @param s the shuffle controlling lane index selection * @return the rearrangement of the lane elements of this vector * @throws IndexOutOfBoundsException if there are any exceptional * source indexes in the shuffle * @see #rearrange(VectorShuffle,VectorMask) * @see #rearrange(VectorShuffle,Vector) * @see VectorShuffle#laneIsValid() */ public abstract Vector<E> rearrange(VectorShuffle<E> s); /** * Rearranges the lane elements of this vector, selecting lanes * under the control of a specific shuffle and a mask. * * This is a cross-lane operation that rearranges the lane * elements of this vector. * * For each lane {@code N} of the shuffle, and for each lane * source index {@code I=s.laneSource(N)} in the shuffle, * the output lane {@code N} obtains the value from * the input vector at lane {@code I} if the mask is set. * Otherwise the output lane {@code N} is set to zero. * * <p> This method returns the value of this pseudocode: * <pre>{@code * Vector<E> r = this.rearrange(s.wrapIndexes()); * VectorMask<E> valid = s.laneIsValid(); * if (m.andNot(valid).anyTrue()) throw ...; * return broadcast(0).blend(r, m); * }</pre> * * @param s the shuffle controlling lane index selection * @param m the mask controlling application of the shuffle * @return the rearrangement of the lane elements of this vector * @throws IndexOutOfBoundsException if there are any exceptional * source indexes in the shuffle where the mask is set * @see #rearrange(VectorShuffle) * @see #rearrange(VectorShuffle,Vector) * @see VectorShuffle#laneIsValid() */ public abstract Vector<E> rearrange(VectorShuffle<E> s, VectorMask<E> m); /** * Rearranges the lane elements of two vectors, selecting lanes * under the control of a specific shuffle, using both normal and * exceptional indexes in the shuffle to steer data. * * This is a cross-lane operation that rearranges the lane * elements of the two input vectors (the current vector * and a second vector {@code v}). * * For each lane {@code N} of the shuffle, and for each lane * source index {@code I=s.laneSource(N)} in the shuffle, * the output lane {@code N} obtains the value from * the first vector at lane {@code I} if {@code I>=0}. * Otherwise, the exceptional index {@code I} is wrapped * by adding {@code VLENGTH} to it and used to index * the <em>second</em> vector, at index {@code I+VLENGTH}. * * <p> This method returns the value of this pseudocode: * <pre>{@code * Vector<E> r1 = this.rearrange(s.wrapIndexes()); * // or else: r1 = this.rearrange(s, s.laneIsValid()); * Vector<E> r2 = v.rearrange(s.wrapIndexes()); * return r2.blend(r1,s.laneIsValid()); * }</pre> * * @param s the shuffle controlling lane selection from both input vectors * @param v the second input vector * @return the rearrangement of lane elements of this vector and * a second input vector * @see #rearrange(VectorShuffle) * @see #rearrange(VectorShuffle,VectorMask) * @see VectorShuffle#laneIsValid() * @see #slice(int,Vector) */ public abstract Vector<E> rearrange(VectorShuffle<E> s, Vector<E> v); /** * Using index values stored in the lanes of this vector, * assemble values stored in second vector {@code v}. * The second vector thus serves as a table, whose * elements are selected by indexes in the current vector. * * This is a cross-lane operation that rearranges the lane * elements of the argument vector, under the control of * this vector. * * For each lane {@code N} of this vector, and for each lane * value {@code I=this.lane(N)} in this vector, * the output lane {@code N} obtains the value from * the argument vector at lane {@code I}. * * In this way, the result contains only values stored in the * argument vector {@code v}, but presented in an order which * depends on the index values in {@code this}. * * The result is the same as the expression * {@code v.rearrange(this.toShuffle())}. * * @param v the vector supplying the result values * @return the rearrangement of the lane elements of {@code v} * @throws IndexOutOfBoundsException if any invalid * source indexes are found in {@code this} * @see #rearrange(VectorShuffle) */ public abstract Vector<E> selectFrom(Vector<E> v); /** * Using index values stored in the lanes of this vector, * assemble values stored in second vector, under the control * of a mask. * Using index values stored in the lanes of this vector, * assemble values stored in second vector {@code v}. * The second vector thus serves as a table, whose * elements are selected by indexes in the current vector. * Lanes that are unset in the mask receive a * zero rather than a value from the table. * * This is a cross-lane operation that rearranges the lane * elements of the argument vector, under the control of * this vector and the mask. * * The result is the same as the expression * {@code v.rearrange(this.toShuffle(), m)}. * * @param v the vector supplying the result values * @param m the mask controlling selection from {@code v} * @return the rearrangement of the lane elements of {@code v} * @throws IndexOutOfBoundsException if any invalid * source indexes are found in {@code this}, * in a lane which is set in the mask * @see #selectFrom(Vector) * @see #rearrange(VectorShuffle,VectorMask) */ public abstract Vector<E> selectFrom(Vector<E> v, VectorMask<E> m); // Conversions /** * Returns a vector of the same species as this one * where all lane elements are set to * the primitive value {@code e}. * * The contents of the current vector are discarded; * only the species is relevant to this operation. * * <p> This method returns the value of this expression: * {@code EVector.broadcast(this.species(), (ETYPE)e)}, where * {@code EVector} is the vector class specific to this * vector's element type {@code ETYPE}. * * <p> * The {@code long} value {@code e} must be accurately * representable by the {@code ETYPE} of this vector's species, * so that {@code e==(long)(ETYPE)e}. * * If this rule is violated the problem is not detected * statically, but an {@code IllegalArgumentException} is thrown * at run-time. Thus, this method somewhat weakens the static * type checking of immediate constants and other scalars, but it * makes up for this by improving the expressiveness of the * generic API. Note that an {@code e} value in the range * {@code [-128..127]} is always acceptable, since every * {@code ETYPE} will accept every {@code byte} value. * * @apiNote * Subtypes improve on this method by sharpening * the method return type and * and the type of the scalar parameter {@code e}. * * @param e the value to broadcast * @return a vector where all lane elements are set to * the primitive value {@code e} * @throws IllegalArgumentException * if the given {@code long} value cannot * be represented by the vector's {@code ETYPE} * @see VectorSpecies#broadcast(long) * @see IntVector#broadcast(int) * @see FloatVector#broadcast(float) */ public abstract Vector<E> broadcast(long e); /** * Returns a mask of same species as this vector, * where each lane is set or unset according to given * single boolean, which is broadcast to all lanes. * <p> * This method returns the value of this expression: * {@code species().maskAll(bit)}. * * @param bit the given mask bit to be replicated * @return a mask where each lane is set or unset according to * the given bit * @see VectorSpecies#maskAll(boolean) */ public abstract VectorMask<E> maskAll(boolean bit); /** * Converts this vector into a shuffle, converting the lane values * to {@code int} and regarding them as source indexes. * <p> * This method behaves as if it returns the result of creating a shuffle * given an array of the vector elements, as follows: * <pre>{@code * long[] a = this.toLongArray(); * int[] sa = new int[a.length]; * for (int i = 0; i < a.length; i++) { * sa[i] = (int) a[i]; * } * return VectorShuffle.fromValues(this.species(), sa); * }</pre> * * @return a shuffle representation of this vector * @see VectorShuffle#fromValues(VectorSpecies,int...) */ public abstract VectorShuffle<E> toShuffle(); // Bitwise preserving /** * Transforms this vector to a vector of the given species of * element type {@code F}, reinterpreting the bytes of this * vector without performing any value conversions. * * <p> Depending on the selected species, this operation may * either <a href="Vector.html#expansion">expand or contract</a> * its logical result, in which case a non-zero {@code part} * number can further control the selection and steering of the * logical result into the physical output vector. * * <p> * The underlying bits of this vector are copied to the resulting * vector without modification, but those bits, before copying, * may be truncated if the this vector's bit-size is greater than * desired vector's bit size, or filled with zero bits if this * vector's bit-size is less than desired vector's bit-size. * * <p> If the old and new species have different shape, this is a * <em>shape-changing</em> operation, and may have special * implementation costs. * * <p> The method behaves as if this vector is stored into a byte * buffer or array using little-endian byte ordering and then the * desired vector is loaded from the same byte buffer or array * using the same ordering. * * <p> The following pseudocode illustrates the behavior: * <pre>{@code * int domSize = this.byteSize(); * int ranSize = species.vectorByteSize(); * int M = (domSize > ranSize ? domSize / ranSize : ranSize / domSize); * assert Math.abs(part) < M; * assert (part == 0) || (part > 0) == (domSize > ranSize); * byte[] ra = new byte[Math.max(domSize, ranSize)]; * if (domSize > ranSize) { // expansion * this.intoByteArray(ra, 0, ByteOrder.native()); * int origin = part * ranSize; * return species.fromByteArray(ra, origin, ByteOrder.native()); * } else { // contraction or size-invariant * int origin = (-part) * domSize; * this.intoByteArray(ra, origin, ByteOrder.native()); * return species.fromByteArray(ra, 0, ByteOrder.native()); * } * }</pre> * * @apiNote Although this method is defined as if the vectors in * question were loaded or stored into memory, memory semantics * has little to do or nothing with the actual implementation. * The appeal to little-endian ordering is simply a shorthand * for what could otherwise be a large number of detailed rules * concerning the mapping between lane-structured vectors and * byte-structured vectors. * * @param species the desired vector species * @param part the <a href="Vector.html#expansion">part number</a> * of the result, or zero if neither expanding nor contracting * @param <F> the boxed element type of the species * @return a vector transformed, by shape and element type, from this vector * @see Vector#convertShape(VectorOperators.Conversion,VectorSpecies,int) * @see Vector#castShape(VectorSpecies,int) * @see VectorSpecies#partLimit(VectorSpecies,boolean) */ public abstract <F> Vector<F> reinterpretShape(VectorSpecies<F> species, int part); /** * Views this vector as a vector of the same shape * and contents but a lane type of {@code byte}, * where the bytes are extracted from the lanes * according to little-endian order. * It is a convenience method for the expression * {@code reinterpretShape(species().withLanes(byte.class))}. * It may be considered an inverse to the various * methods which consolidate bytes into larger lanes * within the same vector, such as * {@link Vector#reinterpretAsInts()}. * * @return a {@code ByteVector} with the same shape and information content * @see Vector#reinterpretShape(VectorSpecies,int) * @see IntVector#intoByteArray(byte[], int, ByteOrder) * @see FloatVector#intoByteArray(byte[], int, ByteOrder) * @see VectorSpecies#withLanes(Class) */ public abstract ByteVector reinterpretAsBytes(); /** * Reinterprets this vector as a vector of the same shape * and contents but a lane type of {@code short}, * where the lanes are assembled from successive bytes * according to little-endian order. * It is a convenience method for the expression * {@code reinterpretShape(species().withLanes(short.class))}. * It may be considered an inverse to {@link Vector#reinterpretAsBytes()}. * * @return a {@code ShortVector} with the same shape and information content */ public abstract ShortVector reinterpretAsShorts(); /** * Reinterprets this vector as a vector of the same shape * and contents but a lane type of {@code int}, * where the lanes are assembled from successive bytes * according to little-endian order. * It is a convenience method for the expression * {@code reinterpretShape(species().withLanes(int.class))}. * It may be considered an inverse to {@link Vector#reinterpretAsBytes()}. * * @return a {@code IntVector} with the same shape and information content */ public abstract IntVector reinterpretAsInts(); /** * Reinterprets this vector as a vector of the same shape * and contents but a lane type of {@code long}, * where the lanes are assembled from successive bytes * according to little-endian order. * It is a convenience method for the expression * {@code reinterpretShape(species().withLanes(long.class))}. * It may be considered an inverse to {@link Vector#reinterpretAsBytes()}. * * @return a {@code LongVector} with the same shape and information content */ public abstract LongVector reinterpretAsLongs(); /** * Reinterprets this vector as a vector of the same shape * and contents but a lane type of {@code float}, * where the lanes are assembled from successive bytes * according to little-endian order. * It is a convenience method for the expression * {@code reinterpretShape(species().withLanes(float.class))}. * It may be considered an inverse to {@link Vector#reinterpretAsBytes()}. * * @return a {@code FloatVector} with the same shape and information content */ public abstract FloatVector reinterpretAsFloats(); /** * Reinterprets this vector as a vector of the same shape * and contents but a lane type of {@code double}, * where the lanes are assembled from successive bytes * according to little-endian order. * It is a convenience method for the expression * {@code reinterpretShape(species().withLanes(double.class))}. * It may be considered an inverse to {@link Vector#reinterpretAsBytes()}. * * @return a {@code DoubleVector} with the same shape and information content */ public abstract DoubleVector reinterpretAsDoubles(); /** * Views this vector as a vector of the same shape, length, and * contents, but a lane type that is not a floating-point type. * * This is a lane-wise reinterpretation cast on the lane values. * As such, this method does not change {@code VSHAPE} or * {@code VLENGTH}, and there is no change to the bitwise contents * of the vector. If the vector's {@code ETYPE} is already an * integral type, the same vector is returned unchanged. * * This method returns the value of this expression: * {@code convert(conv,0)}, where {@code conv} is * {@code VectorOperators.Conversion.ofReinterpret(E.class,F.class)}, * and {@code F} is the non-floating-point type of the * same size as {@code E}. * * @apiNote * Subtypes improve on this method by sharpening * the return type. * * @return the original vector, reinterpreted as non-floating point * @see VectorOperators.Conversion#ofReinterpret(Class,Class) * @see Vector#convert(VectorOperators.Conversion,int) */ public abstract Vector<?> viewAsIntegralLanes(); /** * Views this vector as a vector of the same shape, length, and * contents, but a lane type that is a floating-point type. * * This is a lane-wise reinterpretation cast on the lane values. * As such, there this method does not change {@code VSHAPE} or * {@code VLENGTH}, and there is no change to the bitwise contents * of the vector. If the vector's {@code ETYPE} is already a * float-point type, the same vector is returned unchanged. * * If the vector's element size does not match any floating point * type size, an {@code IllegalArgumentException} is thrown. * * This method returns the value of this expression: * {@code convert(conv,0)}, where {@code conv} is * {@code VectorOperators.Conversion.ofReinterpret(E.class,F.class)}, * and {@code F} is the floating-point type of the * same size as {@code E}, if any. * * @apiNote * Subtypes improve on this method by sharpening * the return type. * * @return the original vector, reinterpreted as floating point * @throws UnsupportedOperationException if there is no floating point * type the same size as the lanes of this vector * @see VectorOperators.Conversion#ofReinterpret(Class,Class) * @see Vector#convert(VectorOperators.Conversion,int) */ public abstract Vector<?> viewAsFloatingLanes(); /** * Convert this vector to a vector of the same shape and a new * element type, converting lane values from the current {@code ETYPE} * to a new lane type (called {@code FTYPE} here) according to the * indicated {@linkplain VectorOperators.Conversion conversion}. * * This is a lane-wise shape-invariant operation which copies * {@code ETYPE} values from the input vector to corresponding * {@code FTYPE} values in the result. Depending on the selected * conversion, this operation may either * <a href="Vector.html#expansion">expand or contract</a> its * logical result, in which case a non-zero {@code part} number * can further control the selection and steering of the logical * result into the physical output vector. * * <p> Each specific conversion is described by a conversion * constant in the class {@link VectorOperators}. Each conversion * operator has a specified {@linkplain * VectorOperators.Conversion#domainType() domain type} and * {@linkplain VectorOperators.Conversion#rangeType() range type}. * The domain type must exactly match the lane type of the input * vector, while the range type determines the lane type of the * output vectors. * * <p> A conversion operator may be classified as (respectively) * in-place, expanding, or contracting, depending on whether the * bit-size of its domain type is (respectively) equal, less than, * or greater than the bit-size of its range type. * * <p> Independently, conversion operations can also be classified * as reinterpreting or value-transforming, depending on whether * the conversion copies representation bits unchanged, or changes * the representation bits in order to retain (part or all of) * the logical value of the input value. * * <p> If a reinterpreting conversion contracts, it will truncate the * upper bits of the input. If it expands, it will pad upper bits * of the output with zero bits, when there are no corresponding * input bits. * * <p> An expanding conversion such as {@code S2I} ({@code short} * value to {@code int}) takes a scalar value and represents it * in a larger format (always with some information redundancy). * * A contracting conversion such as {@code D2F} ({@code double} * value to {@code float}) takes a scalar value and represents it * in a smaller format (always with some information loss). * * Some in-place conversions may also include information loss, * such as {@code L2D} ({@code long} value to {@code double}) * or {@code F2I} ({@code float} value to {@code int}). * * Reinterpreting in-place conversions are not lossy, unless the * bitwise value is somehow not legal in the output type. * Converting the bit-pattern of a {@code NaN} may discard bits * from the {@code NaN}'s significand. * * <p> This classification is important, because, unless otherwise * documented, conversion operations <em>never change vector * shape</em>, regardless of how they may change <em>lane sizes</em>. * * Therefore an <em>expanding</em> conversion cannot store all of its * results in its output vector, because the output vector has fewer * lanes of larger size, in order to have the same overall bit-size as * its input. * * Likewise, a contracting conversion must store its relatively small * results into a subset of the lanes of the output vector, defaulting * the unused lanes to zero. * * <p> As an example, a conversion from {@code byte} to {@code long} * ({@code M=8}) will discard 87.5% of the input values in order to * convert the remaining 12.5% into the roomy {@code long} lanes of * the output vector. The inverse conversion will convert back all of * the large results, but will waste 87.5% of the lanes in the output * vector. * * <em>In-place</em> conversions ({@code M=1}) deliver all of * their results in one output vector, without wasting lanes. * * <p> To manage the details of these * <a href="Vector.html#expansion">expansions and contractions</a>, * a non-zero {@code part} parameter selects partial results from * expansions, or steers the results of contractions into * corresponding locations, as follows: * * <ul> * <li> expanding by {@code M}: {@code part} must be in the range * {@code [0..M-1]}, and selects the block of {@code VLENGTH/M} input * lanes starting at the <em>origin lane</em> at {@code part*VLENGTH/M}. * <p> The {@code VLENGTH/M} output lanes represent a partial * slice of the whole logical result of the conversion, filling * the entire physical output vector. * * <li> contracting by {@code M}: {@code part} must be in the range * {@code [-M+1..0]}, and steers all {@code VLENGTH} input lanes into * the output located at the <em>origin lane</em> {@code -part*VLENGTH}. * There is a total of {@code VLENGTH*M} output lanes, and those not * holding converted input values are filled with zeroes. * * <p> A group of such output vectors, with logical result parts * steered to disjoint blocks, can be reassembled using the * {@linkplain VectorOperators#OR bitwise or} or (for floating * point) the {@link VectorOperators#FIRST_NONZERO FIRST_NONZERO} * operator. * * <li> in-place ({@code M=1}): {@code part} must be zero. * Both vectors have the same {@code VLENGTH}. The result is * always positioned at the <em>origin lane</em> of zero. * * </ul> * * <p> This method is a restricted version of the more general * but less frequently used <em>shape-changing</em> method * {@link #convertShape(VectorOperators.Conversion,VectorSpecies,int) * convertShape()}. * The result of this method is the same as the expression * {@code this.convertShape(conv, rsp, this.broadcast(part))}, * where the output species is * {@code rsp=this.species().withLanes(FTYPE.class)}. * * @param conv the desired scalar conversion to apply lane-wise * @param part the <a href="Vector.html#expansion">part number</a> * of the result, or zero if neither expanding nor contracting * @param <F> the boxed element type of the species * @return a vector converted by shape and element type from this vector * @throws ArrayIndexOutOfBoundsException unless {@code part} is zero, * or else the expansion ratio is {@code M} and * {@code part} is positive and less than {@code M}, * or else the contraction ratio is {@code M} and * {@code part} is negative and greater {@code -M} * * @see VectorOperators#I2L * @see VectorOperators.Conversion#ofCast(Class,Class) * @see VectorSpecies#partLimit(VectorSpecies,boolean) * @see #viewAsFloatingLanes() * @see #viewAsIntegralLanes() * @see #convertShape(VectorOperators.Conversion,VectorSpecies,int) * @see #reinterpretShape(VectorSpecies,int) */ public abstract <F> Vector<F> convert(VectorOperators.Conversion<E,F> conv, int part); /** * Converts this vector to a vector of the given species, shape and * element type, converting lane values from the current {@code ETYPE} * to a new lane type (called {@code FTYPE} here) according to the * indicated {@linkplain VectorOperators.Conversion conversion}. * * This is a lane-wise operation which copies {@code ETYPE} values * from the input vector to corresponding {@code FTYPE} values in * the result. * * <p> If the old and new species have the same shape, the behavior * is exactly the same as the simpler, shape-invariant method * {@link #convert(VectorOperators.Conversion,int) convert()}. * In such cases, the simpler method {@code convert()} should be * used, to make code easier to reason about. * Otherwise, this is a <em>shape-changing</em> operation, and may * have special implementation costs. * * <p> As a combined effect of shape changes and lane size changes, * the input and output species may have different lane counts, causing * <a href="Vector.html#expansion">expansion or contraction</a>. * In this case a non-zero {@code part} parameter selects * partial results from an expanded logical result, or steers * the results of a contracted logical result into a physical * output vector of the required output species. * * <p >The following pseudocode illustrates the behavior of this * method for in-place, expanding, and contracting conversions. * (This pseudocode also applies to the shape-invariant method, * but with shape restrictions on the output species.) * Note that only one of the three code paths is relevant to any * particular combination of conversion operator and shapes. * * <pre>{@code * FTYPE scalar_conversion_op(ETYPE s); * EVector a = ...; * VectorSpecies<F> rsp = ...; * int part = ...; * VectorSpecies<E> dsp = a.species(); * int domlen = dsp.length(); * int ranlen = rsp.length(); * FTYPE[] logical = new FTYPE[domlen]; * for (int i = 0; i < domlen; i++) { * logical[i] = scalar_conversion_op(a.lane(i)); * } * FTYPE[] physical; * if (domlen == ranlen) { // in-place * assert part == 0; //else AIOOBE * physical = logical; * } else if (domlen > ranlen) { // expanding * int M = domlen / ranlen; * assert 0 <= part && part < M; //else AIOOBE * int origin = part * ranlen; * physical = Arrays.copyOfRange(logical, origin, origin + ranlen); * } else { // (domlen < ranlen) // contracting * int M = ranlen / domlen; * assert 0 >= part && part > -M; //else AIOOBE * int origin = -part * domlen; * System.arraycopy(logical, 0, physical, origin, domlen); * } * return FVector.fromArray(ran, physical, 0); * }</pre> * * @param conv the desired scalar conversion to apply lane-wise * @param rsp the desired output species * @param part the <a href="Vector.html#expansion">part number</a> * of the result, or zero if neither expanding nor contracting * @param <F> the boxed element type of the output species * @return a vector converted by element type from this vector * @see #convert(VectorOperators.Conversion,int) * @see #castShape(VectorSpecies,int) * @see #reinterpretShape(VectorSpecies,int) */ public abstract <F> Vector<F> convertShape(VectorOperators.Conversion<E,F> conv, VectorSpecies<F> rsp, int part); /** * Convenience method for converting a vector from one lane type * to another, reshaping as needed when lane sizes change. * * This method returns the value of this expression: * {@code convertShape(conv,rsp,part)}, where {@code conv} is * {@code VectorOperators.Conversion.ofCast(E.class,F.class)}. * * <p> If the old and new species have different shape, this is a * <em>shape-changing</em> operation, and may have special * implementation costs. * * @param rsp the desired output species * @param part the <a href="Vector.html#expansion">part number</a> * of the result, or zero if neither expanding nor contracting * @param <F> the boxed element type of the output species * @return a vector converted by element type from this vector * @see VectorOperators.Conversion#ofCast(Class,Class) * @see Vector#convertShape(VectorOperators.Conversion,VectorSpecies,int) */ // Does this carry its weight? public abstract <F> Vector<F> castShape(VectorSpecies<F> rsp, int part); /** * Checks that this vector has the given element type, * and returns this vector unchanged. * The effect is similar to this pseudocode: * {@code elementType == species().elementType() * ? this * : throw new ClassCastException()}. * * @param elementType the required lane type * @param <F> the boxed element type of the required lane type * @return the same vector * @throws ClassCastException if the vector has the wrong element type * @see VectorSpecies#check(Class) * @see VectorMask#check(Class) * @see Vector#check(VectorSpecies) * @see VectorShuffle#check(VectorSpecies) */ public abstract <F> Vector<F> check(Class<F> elementType); /** * Checks that this vector has the given species, * and returns this vector unchanged. * The effect is similar to this pseudocode: * {@code species == species() * ? this * : throw new ClassCastException()}. * * @param species the required species * @param <F> the boxed element type of the required species * @return the same vector * @throws ClassCastException if the vector has the wrong species * @see Vector#check(Class) * @see VectorMask#check(VectorSpecies) * @see VectorShuffle#check(VectorSpecies) */ public abstract <F> Vector<F> check(VectorSpecies<F> species); //Array stores /** * Stores this vector into a byte array starting at an offset * using explicit byte order. * <p> * Bytes are extracted from primitive lane elements according * to the specified byte ordering. * The lanes are stored according to their * <a href="Vector.html#lane-order">memory ordering</a>. * <p> * This method behaves as if it calls * {@link #intoByteBuffer(ByteBuffer,int,ByteOrder,VectorMask) * intoByteBuffer()} as follows: * <pre>{@code * var bb = ByteBuffer.wrap(a); * var m = maskAll(true); * intoByteBuffer(bb, offset, bo, m); * }</pre> * * @param a the byte array * @param offset the offset into the array * @param bo the intended byte order * @throws IndexOutOfBoundsException * if {@code offset+N*ESIZE < 0} * or {@code offset+(N+1)*ESIZE > a.length} * for any lane {@code N} in the vector */ public abstract void intoByteArray(byte[] a, int offset, ByteOrder bo); /** * Stores this vector into a byte array starting at an offset * using explicit byte order and a mask. * <p> * Bytes are extracted from primitive lane elements according * to the specified byte ordering. * The lanes are stored according to their * <a href="Vector.html#lane-order">memory ordering</a>. * <p> * This method behaves as if it calls * {@link #intoByteBuffer(ByteBuffer,int,ByteOrder,VectorMask) * intoByteBuffer()} as follows: * <pre>{@code * var bb = ByteBuffer.wrap(a); * intoByteBuffer(bb, offset, bo, m); * }</pre> * * @param a the byte array * @param offset the offset into the array * @param bo the intended byte order * @param m the mask controlling lane selection * @throws IndexOutOfBoundsException * if {@code offset+N*ESIZE < 0} * or {@code offset+(N+1)*ESIZE > a.length} * for any lane {@code N} in the vector * where the mask is set */ public abstract void intoByteArray(byte[] a, int offset, ByteOrder bo, VectorMask<E> m); /** * Stores this vector into a byte buffer starting at an offset * using explicit byte order. * <p> * Bytes are extracted from primitive lane elements according * to the specified byte ordering. * The lanes are stored according to their * <a href="Vector.html#lane-order">memory ordering</a>. * <p> * This method behaves as if it calls * {@link #intoByteBuffer(ByteBuffer,int,ByteOrder,VectorMask) * intoByteBuffer()} as follows: * <pre>{@code * var m = maskAll(true); * intoByteBuffer(bb, offset, bo, m); * }</pre> * * @param bb the byte buffer * @param offset the offset into the array * @param bo the intended byte order * @throws IndexOutOfBoundsException * if {@code offset+N*ESIZE < 0} * or {@code offset+(N+1)*ESIZE > bb.limit()} * for any lane {@code N} in the vector * @throws java.nio.ReadOnlyBufferException * if the byte buffer is read-only */ public abstract void intoByteBuffer(ByteBuffer bb, int offset, ByteOrder bo); /** * Stores this vector into a byte buffer starting at an offset * using explicit byte order and a mask. * <p> * Bytes are extracted from primitive lane elements according * to the specified byte ordering. * The lanes are stored according to their * <a href="Vector.html#lane-order">memory ordering</a>. * <p> * The following pseudocode illustrates the behavior, where * the primitive element type is not of {@code byte}, * {@code EBuffer} is the primitive buffer type, {@code ETYPE} is the * primitive element type, and {@code EVector} is the primitive * vector type for this vector: * <pre>{@code * EBuffer eb = bb.duplicate() * .position(offset) * .order(bo).asEBuffer(); * ETYPE[] a = this.toArray(); * for (int n = 0; n < a.length; n++) { * if (m.laneIsSet(n)) { * eb.put(n, a[n]); * } * } * }</pre> * When the primitive element type is of {@code byte} the primitive * byte buffer is obtained as follows, where operation on the buffer * remains the same as in the prior pseudocode: * <pre>{@code * ByteBuffer eb = bb.duplicate() * .position(offset); * }</pre> * * @implNote * This operation is likely to be more efficient if * the specified byte order is the same as * {@linkplain ByteOrder#nativeOrder() * the platform native order}, * since this method will not need to reorder * the bytes of lane values. * In the special case where {@code ETYPE} is * {@code byte}, the byte order argument is * ignored. * * @param bb the byte buffer * @param offset the offset into the array * @param bo the intended byte order * @param m the mask controlling lane selection * @throws IndexOutOfBoundsException * if {@code offset+N*ESIZE < 0} * or {@code offset+(N+1)*ESIZE > bb.limit()} * for any lane {@code N} in the vector * where the mask is set * @throws java.nio.ReadOnlyBufferException * if the byte buffer is read-only */ public abstract void intoByteBuffer(ByteBuffer bb, int offset, ByteOrder bo, VectorMask<E> m); /** * Returns a packed array containing all the lane values. * The array length is the same as the vector length. * The element type of the array is the same as the element * type of the vector. * The array elements are stored in lane order. * Overrides of this method on subtypes of {@code Vector} * which specify the element type have an accurately typed * array result. * * @apiNote * Usually {@linkplain FloatVector#toArray() strongly typed access} * is preferable, if you are working with a vector * subtype that has a known element type. * * @return an accurately typed array containing * the lane values of this vector * @see ByteVector#toArray() * @see IntVector#toArray() * @see DoubleVector#toArray() */ public abstract Object toArray(); /** * Returns an {@code int[]} array containing all * the lane values, converted to the type {@code int}. * The array length is the same as the vector length. * The array elements are converted as if by casting * and stored in lane order. * * This operation may fail if the vector element type is {@code * float} or {@code double}, when lanes contain fractional or * out-of-range values. If any vector lane value is not * representable as an {@code int}, an exception is thrown. * * @apiNote * Usually {@linkplain FloatVector#toArray() strongly typed access} * is preferable, if you are working with a vector * subtype that has a known element type. * * @return an {@code int[]} array containing * the lane values of this vector * @throws UnsupportedOperationException * if any lane value cannot be represented as an * {@code int} array element * @see #toArray() * @see #toLongArray() * @see #toDoubleArray() * @see IntVector#toArray() */ public abstract int[] toIntArray(); /** * Returns a {@code long[]} array containing all * the lane values, converted to the type {@code long}. * The array length is the same as the vector length. * The array elements are converted as if by casting * and stored in lane order. * * This operation may fail if the vector element type is {@code * float} or {@code double}, when lanes contain fractional or * out-of-range values. If any vector lane value is not * representable as a {@code long}, an exception is thrown. * * @apiNote * Usually {@linkplain FloatVector#toArray() strongly typed access} * is preferable, if you are working with a vector * subtype that has a known element type. * * @return a {@code long[]} array containing * the lane values of this vector * @throws UnsupportedOperationException * if any lane value cannot be represented as a * {@code long} array element * @see #toArray() * @see #toIntArray() * @see #toDoubleArray() * @see LongVector#toArray() */ public abstract long[] toLongArray(); /** * Returns a {@code double[]} array containing all * the lane values, converted to the type {@code double}. * The array length is the same as the vector length. * The array elements are converted as if by casting * and stored in lane order. * This operation can lose precision * if the vector element type is {@code long}. * * @apiNote * Usually {@link FloatVector#toArray() strongly typed access} * is preferable, if you are working with a vector * subtype that has a known element type. * * @return a {@code double[]} array containing * the lane values of this vector, * possibly rounded to representable * {@code double} values * @see #toArray() * @see #toIntArray() * @see #toLongArray() * @see DoubleVector#toArray() */ public abstract double[] toDoubleArray(); /** * Returns a string representation of this vector, of the form * {@code "[0,1,2...]"}, reporting the lane values of this * vector, in lane order. * * The string is produced as if by a call to * {@link Arrays#toString(int[]) Arrays.toString()}, * as appropriate to the array returned by * {@link #toArray() this.toArray()}. * * @return a string of the form {@code "[0,1,2...]"} * reporting the lane values of this vector */ @Override public abstract String toString(); /** * Indicates whether this vector is identical to some other object. * Two vectors are identical only if they have the same species * and same lane values, in the same order. * <p>The comparison of lane values is produced as if by a call to * {@link Arrays#equals(int[],int[]) Arrays.equals()}, * as appropriate to the arrays returned by * {@link #toArray toArray()} on both vectors. * * @return whether this vector is identical to some other object * @see #eq */ @Override public abstract boolean equals(Object obj); /** * Returns a hash code value for the vector. * based on the lane values and the vector species. * * @return a hash code value for this vector */ @Override public abstract int hashCode(); // ==== JROSE NAME CHANGES ==== // RAISED FROM SUBCLASSES (with generalized type) // * toArray() -> ETYPE[] <: Object (erased return type for interop) // * toString(), equals(Object), hashCode() (documented) // ADDED // * compare(OP,v) to replace most of the comparison methods // * maskAll(boolean) to replace maskAllTrue/False // * toLongArray(), toDoubleArray() (generic unboxed access) // * check(Class), check(VectorSpecies) (static type-safety checks) // * enum Comparison (enum of EQ, NE, GT, LT, GE, LE) // * zero(VS), broadcast(long) (basic factories) // * reinterpretAsEs(), viewAsXLanes (bytewise reinterpreting views) // * addIndex(int) (iota function) }
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