1 /*
2 * Copyright (c) 2017, Oracle and/or its affiliates. All rights reserved.
3 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
4 *
5 * This code is free software; you can redistribute it and/or modify it
6 * under the terms of the GNU General Public License version 2 only, as
7 * published by the Free Software Foundation. Oracle designates this
8 * particular file as subject to the "Classpath" exception as provided
9 * by Oracle in the LICENSE file that accompanied this code.
10 *
11 * This code is distributed in the hope that it will be useful, but WITHOUT
12 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
13 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
14 * version 2 for more details (a copy is included in the LICENSE file that
15 * accompanied this code).
16 *
17 * You should have received a copy of the GNU General Public License version
18 * 2 along with this work; if not, write to the Free Software Foundation,
19 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
20 *
21 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
22 * or visit www.oracle.com if you need additional information or have
23 * questions.
24 */
25 package jdk.incubator.vector;
26
27 import java.nio.ByteBuffer;
28 import java.nio.DoubleBuffer;
29 import java.nio.ByteOrder;
30 import java.util.Objects;
31 import java.util.function.IntUnaryOperator;
32 import java.util.function.Function;
33 import java.util.concurrent.ThreadLocalRandom;
34
35 import jdk.internal.misc.Unsafe;
36 import jdk.internal.vm.annotation.ForceInline;
37 import static jdk.incubator.vector.VectorIntrinsics.*;
38
39
40 /**
41 * A specialized {@link Vector} representing an ordered immutable sequence of
42 * {@code double} values.
43 */
44 @SuppressWarnings("cast")
45 public abstract class DoubleVector extends Vector<Double> {
46
47 DoubleVector() {}
48
49 private static final int ARRAY_SHIFT = 31 - Integer.numberOfLeadingZeros(Unsafe.ARRAY_DOUBLE_INDEX_SCALE);
50
51 // Unary operator
52
53 interface FUnOp {
54 double apply(int i, double a);
55 }
56
57 abstract DoubleVector uOp(FUnOp f);
58
59 abstract DoubleVector uOp(Mask<Double> m, FUnOp f);
60
61 // Binary operator
62
63 interface FBinOp {
64 double apply(int i, double a, double b);
65 }
66
67 abstract DoubleVector bOp(Vector<Double> v, FBinOp f);
68
69 abstract DoubleVector bOp(Vector<Double> v, Mask<Double> m, FBinOp f);
70
71 // Trinary operator
72
73 interface FTriOp {
74 double apply(int i, double a, double b, double c);
75 }
76
77 abstract DoubleVector tOp(Vector<Double> v1, Vector<Double> v2, FTriOp f);
78
79 abstract DoubleVector tOp(Vector<Double> v1, Vector<Double> v2, Mask<Double> m, FTriOp f);
80
81 // Reduction operator
82
83 abstract double rOp(double v, FBinOp f);
84
85 // Binary test
86
87 interface FBinTest {
88 boolean apply(int i, double a, double b);
89 }
90
91 abstract Mask<Double> bTest(Vector<Double> v, FBinTest f);
92
93 // Foreach
94
95 interface FUnCon {
96 void apply(int i, double a);
97 }
98
99 abstract void forEach(FUnCon f);
100
101 abstract void forEach(Mask<Double> m, FUnCon f);
102
103 // Static factories
104
105 /**
106 * Returns a vector where all lane elements are set to the default
107 * primitive value.
108 *
109 * @param species species of desired vector
110 * @return a zero vector of given species
111 */
112 @ForceInline
113 @SuppressWarnings("unchecked")
114 public static DoubleVector zero(Species<Double> species) {
115 return VectorIntrinsics.broadcastCoerced((Class<DoubleVector>) species.boxType(), double.class, species.length(),
116 Double.doubleToLongBits(0.0f), species,
117 ((bits, s) -> ((DoubleSpecies)s).op(i -> Double.longBitsToDouble((long)bits))));
118 }
119
120 /**
121 * Loads a vector from a byte array starting at an offset.
122 * <p>
123 * Bytes are composed into primitive lane elements according to the
124 * native byte order of the underlying platform
125 * <p>
126 * This method behaves as if it returns the result of calling the
127 * byte buffer, offset, and mask accepting
128 * {@link #fromByteBuffer(Species<Double>, ByteBuffer, int, Mask) method} as follows:
129 * <pre>{@code
130 * return this.fromByteBuffer(ByteBuffer.wrap(a), i, this.maskAllTrue());
131 * }</pre>
132 *
133 * @param species species of desired vector
134 * @param a the byte array
135 * @param ix the offset into the array
136 * @return a vector loaded from a byte array
137 * @throws IndexOutOfBoundsException if {@code i < 0} or
138 * {@code i > a.length - (this.length() * this.elementSize() / Byte.SIZE)}
139 */
140 @ForceInline
141 @SuppressWarnings("unchecked")
142 public static DoubleVector fromByteArray(Species<Double> species, byte[] a, int ix) {
143 Objects.requireNonNull(a);
144 ix = VectorIntrinsics.checkIndex(ix, a.length, species.bitSize() / Byte.SIZE);
145 return VectorIntrinsics.load((Class<DoubleVector>) species.boxType(), double.class, species.length(),
146 a, ((long) ix) + Unsafe.ARRAY_BYTE_BASE_OFFSET,
147 a, ix, species,
148 (c, idx, s) -> {
149 ByteBuffer bbc = ByteBuffer.wrap(c, idx, a.length - idx).order(ByteOrder.nativeOrder());
150 DoubleBuffer tb = bbc.asDoubleBuffer();
151 return ((DoubleSpecies)s).op(i -> tb.get());
152 });
153 }
154
155 /**
156 * Loads a vector from a byte array starting at an offset and using a
157 * mask.
158 * <p>
159 * Bytes are composed into primitive lane elements according to the
160 * native byte order of the underlying platform.
161 * <p>
162 * This method behaves as if it returns the result of calling the
163 * byte buffer, offset, and mask accepting
164 * {@link #fromByteBuffer(Species<Double>, ByteBuffer, int, Mask) method} as follows:
165 * <pre>{@code
166 * return this.fromByteBuffer(ByteBuffer.wrap(a), i, m);
167 * }</pre>
168 *
169 * @param species species of desired vector
170 * @param a the byte array
171 * @param ix the offset into the array
172 * @param m the mask
173 * @return a vector loaded from a byte array
174 * @throws IndexOutOfBoundsException if {@code i < 0} or
175 * {@code i > a.length - (this.length() * this.elementSize() / Byte.SIZE)}
176 * @throws IndexOutOfBoundsException if the offset is {@code < 0},
177 * or {@code > a.length},
178 * for any vector lane index {@code N} where the mask at lane {@code N}
179 * is set
180 * {@code i >= a.length - (N * this.elementSize() / Byte.SIZE)}
181 */
182 @ForceInline
183 public static DoubleVector fromByteArray(Species<Double> species, byte[] a, int ix, Mask<Double> m) {
184 return zero(species).blend(fromByteArray(species, a, ix), m);
185 }
186
187 /**
188 * Loads a vector from an array starting at offset.
189 * <p>
190 * For each vector lane, where {@code N} is the vector lane index, the
191 * array element at index {@code i + N} is placed into the
192 * resulting vector at lane index {@code N}.
193 *
194 * @param species species of desired vector
195 * @param a the array
196 * @param i the offset into the array
197 * @return the vector loaded from an array
198 * @throws IndexOutOfBoundsException if {@code i < 0}, or
199 * {@code i > a.length - this.length()}
200 */
201 @ForceInline
202 @SuppressWarnings("unchecked")
203 public static DoubleVector fromArray(Species<Double> species, double[] a, int i){
204 Objects.requireNonNull(a);
205 i = VectorIntrinsics.checkIndex(i, a.length, species.length());
206 return VectorIntrinsics.load((Class<DoubleVector>) species.boxType(), double.class, species.length(),
207 a, (((long) i) << ARRAY_SHIFT) + Unsafe.ARRAY_DOUBLE_BASE_OFFSET,
208 a, i, species,
209 (c, idx, s) -> ((DoubleSpecies)s).op(n -> c[idx + n]));
210 }
211
212
213 /**
214 * Loads a vector from an array starting at offset and using a mask.
215 * <p>
216 * For each vector lane, where {@code N} is the vector lane index,
217 * if the mask lane at index {@code N} is set then the array element at
218 * index {@code i + N} is placed into the resulting vector at lane index
219 * {@code N}, otherwise the default element value is placed into the
220 * resulting vector at lane index {@code N}.
221 *
222 * @param species species of desired vector
223 * @param a the array
224 * @param i the offset into the array
225 * @param m the mask
226 * @return the vector loaded from an array
227 * @throws IndexOutOfBoundsException if {@code i < 0}, or
228 * for any vector lane index {@code N} where the mask at lane {@code N}
229 * is set {@code i > a.length - N}
230 */
231 @ForceInline
232 public static DoubleVector fromArray(Species<Double> species, double[] a, int i, Mask<Double> m) {
233 return zero(species).blend(fromArray(species, a, i), m);
234 }
235
236 /**
237 * Loads a vector from an array using indexes obtained from an index
238 * map.
239 * <p>
240 * For each vector lane, where {@code N} is the vector lane index, the
241 * array element at index {@code i + indexMap[j + N]} is placed into the
242 * resulting vector at lane index {@code N}.
243 *
244 * @param species species of desired vector
245 * @param a the array
246 * @param i the offset into the array, may be negative if relative
247 * indexes in the index map compensate to produce a value within the
248 * array bounds
249 * @param indexMap the index map
250 * @param j the offset into the index map
251 * @return the vector loaded from an array
252 * @throws IndexOutOfBoundsException if {@code j < 0}, or
253 * {@code j > indexMap.length - this.length()},
254 * or for any vector lane index {@code N} the result of
255 * {@code i + indexMap[j + N]} is {@code < 0} or {@code >= a.length}
256 */
257 @ForceInline
258 @SuppressWarnings("unchecked")
259 public static DoubleVector fromArray(Species<Double> species, double[] a, int i, int[] indexMap, int j) {
260 Objects.requireNonNull(a);
261 Objects.requireNonNull(indexMap);
262
263 if (species.length() == 1) {
264 return DoubleVector.fromArray(species, a, i + indexMap[j]);
265 }
266
267 // Index vector: vix[0:n] = k -> i + indexMap[j + k]
268 IntVector vix = IntVector.fromArray(IntVector.species(species.indexShape()), indexMap, j).add(i);
269
270 vix = VectorIntrinsics.checkIndex(vix, a.length);
271
272 return VectorIntrinsics.loadWithMap((Class<DoubleVector>) species.boxType(), double.class, species.length(),
273 IntVector.species(species.indexShape()).boxType(), a, Unsafe.ARRAY_DOUBLE_BASE_OFFSET, vix,
274 a, i, indexMap, j, species,
275 (double[] c, int idx, int[] iMap, int idy, Species<Double> s) ->
276 ((DoubleSpecies)s).op(n -> c[idx + iMap[idy+n]]));
277 }
278
279 /**
280 * Loads a vector from an array using indexes obtained from an index
281 * map and using a mask.
282 * <p>
283 * For each vector lane, where {@code N} is the vector lane index,
284 * if the mask lane at index {@code N} is set then the array element at
285 * index {@code i + indexMap[j + N]} is placed into the resulting vector
286 * at lane index {@code N}.
287 *
288 * @param species species of desired vector
289 * @param a the array
290 * @param i the offset into the array, may be negative if relative
291 * indexes in the index map compensate to produce a value within the
292 * array bounds
293 * @param m the mask
294 * @param indexMap the index map
295 * @param j the offset into the index map
296 * @return the vector loaded from an array
297 * @throws IndexOutOfBoundsException if {@code j < 0}, or
298 * {@code j > indexMap.length - this.length()},
299 * or for any vector lane index {@code N} where the mask at lane
300 * {@code N} is set the result of {@code i + indexMap[j + N]} is
301 * {@code < 0} or {@code >= a.length}
302 */
303 @ForceInline
304 @SuppressWarnings("unchecked")
305 public static DoubleVector fromArray(Species<Double> species, double[] a, int i, Mask<Double> m, int[] indexMap, int j) {
306 // @@@ This can result in out of bounds errors for unset mask lanes
307 return zero(species).blend(fromArray(species, a, i, indexMap, j), m);
308 }
309
310
311 /**
312 * Loads a vector from a {@link ByteBuffer byte buffer} starting at an
313 * offset into the byte buffer.
314 * <p>
315 * Bytes are composed into primitive lane elements according to the
316 * native byte order of the underlying platform.
317 * <p>
318 * This method behaves as if it returns the result of calling the
319 * byte buffer, offset, and mask accepting
320 * {@link #fromByteBuffer(Species<Double>, ByteBuffer, int, Mask)} method} as follows:
321 * <pre>{@code
322 * return this.fromByteBuffer(b, i, this.maskAllTrue())
323 * }</pre>
324 *
325 * @param species species of desired vector
326 * @param bb the byte buffer
327 * @param ix the offset into the byte buffer
328 * @return a vector loaded from a byte buffer
329 * @throws IndexOutOfBoundsException if the offset is {@code < 0},
330 * or {@code > b.limit()},
331 * or if there are fewer than
332 * {@code this.length() * this.elementSize() / Byte.SIZE} bytes
333 * remaining in the byte buffer from the given offset
334 */
335 @ForceInline
336 @SuppressWarnings("unchecked")
337 public static DoubleVector fromByteBuffer(Species<Double> species, ByteBuffer bb, int ix) {
338 if (bb.order() != ByteOrder.nativeOrder()) {
339 throw new IllegalArgumentException();
340 }
341 ix = VectorIntrinsics.checkIndex(ix, bb.limit(), species.bitSize() / Byte.SIZE);
342 return VectorIntrinsics.load((Class<DoubleVector>) species.boxType(), double.class, species.length(),
343 U.getReference(bb, BYTE_BUFFER_HB), U.getLong(bb, BUFFER_ADDRESS) + ix,
344 bb, ix, species,
345 (c, idx, s) -> {
346 ByteBuffer bbc = c.duplicate().position(idx).order(ByteOrder.nativeOrder());
347 DoubleBuffer tb = bbc.asDoubleBuffer();
348 return ((DoubleSpecies)s).op(i -> tb.get());
349 });
350 }
351
352 /**
353 * Loads a vector from a {@link ByteBuffer byte buffer} starting at an
354 * offset into the byte buffer and using a mask.
355 * <p>
356 * This method behaves as if the byte buffer is viewed as a primitive
357 * {@link java.nio.Buffer buffer} for the primitive element type,
358 * according to the native byte order of the underlying platform, and
359 * the returned vector is loaded with a mask from a primitive array
360 * obtained from the primitive buffer.
361 * The following pseudocode expresses the behaviour, where
362 * {@coce EBuffer} is the primitive buffer type, {@code e} is the
363 * primitive element type, and {@code ESpecies<S>} is the primitive
364 * species for {@code e}:
365 * <pre>{@code
366 * EBuffer eb = b.duplicate().
367 * order(ByteOrder.nativeOrder()).position(i).
368 * asEBuffer();
369 * e[] es = new e[this.length()];
370 * for (int n = 0; n < t.length; n++) {
371 * if (m.isSet(n))
372 * es[n] = eb.get(n);
373 * }
374 * Vector<E> r = ((ESpecies<S>)this).fromArray(es, 0, m);
375 * }</pre>
376 *
377 * @param species species of desired vector
378 * @param bb the byte buffer
379 * @param ix the offset into the byte buffer
380 * @param m the mask
381 * @return a vector loaded from a byte buffer
382 * @throws IndexOutOfBoundsException if the offset is {@code < 0},
383 * or {@code > b.limit()},
384 * for any vector lane index {@code N} where the mask at lane {@code N}
385 * is set
386 * {@code i >= b.limit() - (N * this.elementSize() / Byte.SIZE)}
387 */
388 @ForceInline
389 public static DoubleVector fromByteBuffer(Species<Double> species, ByteBuffer bb, int ix, Mask<Double> m) {
390 return zero(species).blend(fromByteBuffer(species, bb, ix), m);
391 }
392
393 /**
394 * Returns a vector where all lane elements are set to the primitive
395 * value {@code e}.
396 *
397 * @param s species of the desired vector
398 * @param e the value
399 * @return a vector of vector where all lane elements are set to
400 * the primitive value {@code e}
401 */
402 @ForceInline
403 @SuppressWarnings("unchecked")
404 public static DoubleVector broadcast(Species<Double> s, double e) {
405 return VectorIntrinsics.broadcastCoerced(
406 (Class<DoubleVector>) s.boxType(), double.class, s.length(),
407 Double.doubleToLongBits(e), s,
408 ((bits, sp) -> ((DoubleSpecies)sp).op(i -> Double.longBitsToDouble((long)bits))));
409 }
410
411 /**
412 * Returns a vector where each lane element is set to a given
413 * primitive value.
414 * <p>
415 * For each vector lane, where {@code N} is the vector lane index, the
416 * the primitive value at index {@code N} is placed into the resulting
417 * vector at lane index {@code N}.
418 *
419 * @param s species of the desired vector
420 * @param es the given primitive values
421 * @return a vector where each lane element is set to a given primitive
422 * value
423 * @throws IndexOutOfBoundsException if {@code es.length < this.length()}
424 */
425 @ForceInline
426 @SuppressWarnings("unchecked")
427 public static DoubleVector scalars(Species<Double> s, double... es) {
428 Objects.requireNonNull(es);
429 int ix = VectorIntrinsics.checkIndex(0, es.length, s.length());
430 return VectorIntrinsics.load((Class<DoubleVector>) s.boxType(), double.class, s.length(),
431 es, Unsafe.ARRAY_DOUBLE_BASE_OFFSET,
432 es, ix, s,
433 (c, idx, sp) -> ((DoubleSpecies)sp).op(n -> c[idx + n]));
434 }
435
436 /**
437 * Returns a vector where the first lane element is set to the primtive
438 * value {@code e}, all other lane elements are set to the default
439 * value.
440 *
441 * @param s species of the desired vector
442 * @param e the value
443 * @return a vector where the first lane element is set to the primitive
444 * value {@code e}
445 */
446 @ForceInline
447 public static final DoubleVector single(Species<Double> s, double e) {
448 return zero(s).with(0, e);
449 }
450
451 /**
452 * Returns a vector where each lane element is set to a randomly
453 * generated primitive value.
454 *
455 * The semantics are equivalent to calling
456 * {@link ThreadLocalRandom#nextDouble()}
457 *
458 * @param s species of the desired vector
459 * @return a vector where each lane elements is set to a randomly
460 * generated primitive value
461 */
462 public static DoubleVector random(Species<Double> s) {
463 ThreadLocalRandom r = ThreadLocalRandom.current();
464 return ((DoubleSpecies)s).op(i -> r.nextDouble());
465 }
466
467 /**
468 * Returns a mask where each lane is set or unset according to given
469 * {@code boolean} values
470 * <p>
471 * For each mask lane, where {@code N} is the mask lane index,
472 * if the given {@code boolean} value at index {@code N} is {@code true}
473 * then the mask lane at index {@code N} is set, otherwise it is unset.
474 *
475 * @param species mask species
476 * @param bits the given {@code boolean} values
477 * @return a mask where each lane is set or unset according to the given {@code boolean} value
478 * @throws IndexOutOfBoundsException if {@code bits.length < species.length()}
479 */
480 @ForceInline
481 public static Mask<Double> maskFromValues(Species<Double> species, boolean... bits) {
482 if (species.boxType() == DoubleMaxVector.class)
483 return new DoubleMaxVector.DoubleMaxMask(bits);
484 switch (species.bitSize()) {
485 case 64: return new Double64Vector.Double64Mask(bits);
486 case 128: return new Double128Vector.Double128Mask(bits);
487 case 256: return new Double256Vector.Double256Mask(bits);
488 case 512: return new Double512Vector.Double512Mask(bits);
489 default: throw new IllegalArgumentException(Integer.toString(species.bitSize()));
490 }
491 }
492
493 // @@@ This is a bad implementation -- makes lambdas capturing -- fix this
494 static Mask<Double> trueMask(Species<Double> species) {
495 if (species.boxType() == DoubleMaxVector.class)
496 return DoubleMaxVector.DoubleMaxMask.TRUE_MASK;
497 switch (species.bitSize()) {
498 case 64: return Double64Vector.Double64Mask.TRUE_MASK;
499 case 128: return Double128Vector.Double128Mask.TRUE_MASK;
500 case 256: return Double256Vector.Double256Mask.TRUE_MASK;
501 case 512: return Double512Vector.Double512Mask.TRUE_MASK;
502 default: throw new IllegalArgumentException(Integer.toString(species.bitSize()));
503 }
504 }
505
506 static Mask<Double> falseMask(Species<Double> species) {
507 if (species.boxType() == DoubleMaxVector.class)
508 return DoubleMaxVector.DoubleMaxMask.FALSE_MASK;
509 switch (species.bitSize()) {
510 case 64: return Double64Vector.Double64Mask.FALSE_MASK;
511 case 128: return Double128Vector.Double128Mask.FALSE_MASK;
512 case 256: return Double256Vector.Double256Mask.FALSE_MASK;
513 case 512: return Double512Vector.Double512Mask.FALSE_MASK;
514 default: throw new IllegalArgumentException(Integer.toString(species.bitSize()));
515 }
516 }
517
518 /**
519 * Loads a mask from a {@code boolean} array starting at an offset.
520 * <p>
521 * For each mask lane, where {@code N} is the mask lane index,
522 * if the array element at index {@code ix + N} is {@code true} then the
523 * mask lane at index {@code N} is set, otherwise it is unset.
524 *
525 * @param species mask species
526 * @param bits the {@code boolean} array
527 * @param ix the offset into the array
528 * @return the mask loaded from a {@code boolean} array
529 * @throws IndexOutOfBoundsException if {@code ix < 0}, or
530 * {@code ix > bits.length - species.length()}
531 */
532 @ForceInline
533 @SuppressWarnings("unchecked")
534 public static Mask<Double> maskFromArray(Species<Double> species, boolean[] bits, int ix) {
535 Objects.requireNonNull(bits);
536 ix = VectorIntrinsics.checkIndex(ix, bits.length, species.length());
537 return VectorIntrinsics.load((Class<Mask<Double>>) species.maskType(), long.class, species.length(),
538 bits, (((long) ix) << ARRAY_SHIFT) + Unsafe.ARRAY_BOOLEAN_BASE_OFFSET,
539 bits, ix, species,
540 (c, idx, s) -> (Mask<Double>) ((DoubleSpecies)s).opm(n -> c[idx + n]));
541 }
542
543 /**
544 * Returns a mask where all lanes are set.
545 *
546 * @param species mask species
547 * @return a mask where all lanes are set
548 */
549 @ForceInline
550 @SuppressWarnings("unchecked")
551 public static Mask<Double> maskAllTrue(Species<Double> species) {
552 return VectorIntrinsics.broadcastCoerced((Class<Mask<Double>>) species.maskType(), long.class, species.length(),
553 (long)-1, species,
554 ((z, s) -> trueMask(s)));
555 }
556
557 /**
558 * Returns a mask where all lanes are unset.
559 *
560 * @param species mask species
561 * @return a mask where all lanes are unset
562 */
563 @ForceInline
564 @SuppressWarnings("unchecked")
565 public static Mask<Double> maskAllFalse(Species<Double> species) {
566 return VectorIntrinsics.broadcastCoerced((Class<Mask<Double>>) species.maskType(), long.class, species.length(),
567 0, species,
568 ((z, s) -> falseMask(s)));
569 }
570
571 /**
572 * Returns a shuffle of mapped indexes where each lane element is
573 * the result of applying a mapping function to the corresponding lane
574 * index.
575 * <p>
576 * Care should be taken to ensure Shuffle values produced from this
577 * method are consumed as constants to ensure optimal generation of
578 * code. For example, values held in static final fields or values
579 * held in loop constant local variables.
580 * <p>
581 * This method behaves as if a shuffle is created from an array of
582 * mapped indexes as follows:
583 * <pre>{@code
584 * int[] a = new int[species.length()];
585 * for (int i = 0; i < a.length; i++) {
586 * a[i] = f.applyAsInt(i);
587 * }
588 * return this.shuffleFromValues(a);
589 * }</pre>
590 *
591 * @param species shuffle species
592 * @param f the lane index mapping function
593 * @return a shuffle of mapped indexes
594 */
595 @ForceInline
596 public static Shuffle<Double> shuffle(Species<Double> species, IntUnaryOperator f) {
597 if (species.boxType() == DoubleMaxVector.class)
598 return new DoubleMaxVector.DoubleMaxShuffle(f);
599 switch (species.bitSize()) {
600 case 64: return new Double64Vector.Double64Shuffle(f);
601 case 128: return new Double128Vector.Double128Shuffle(f);
602 case 256: return new Double256Vector.Double256Shuffle(f);
603 case 512: return new Double512Vector.Double512Shuffle(f);
604 default: throw new IllegalArgumentException(Integer.toString(species.bitSize()));
605 }
606 }
607
608 /**
609 * Returns a shuffle where each lane element is the value of its
610 * corresponding lane index.
611 * <p>
612 * This method behaves as if a shuffle is created from an identity
613 * index mapping function as follows:
614 * <pre>{@code
615 * return this.shuffle(i -> i);
616 * }</pre>
617 *
618 * @param species shuffle species
619 * @return a shuffle of lane indexes
620 */
621 @ForceInline
622 public static Shuffle<Double> shuffleIota(Species<Double> species) {
623 if (species.boxType() == DoubleMaxVector.class)
624 return new DoubleMaxVector.DoubleMaxShuffle(AbstractShuffle.IDENTITY);
625 switch (species.bitSize()) {
626 case 64: return new Double64Vector.Double64Shuffle(AbstractShuffle.IDENTITY);
627 case 128: return new Double128Vector.Double128Shuffle(AbstractShuffle.IDENTITY);
628 case 256: return new Double256Vector.Double256Shuffle(AbstractShuffle.IDENTITY);
629 case 512: return new Double512Vector.Double512Shuffle(AbstractShuffle.IDENTITY);
630 default: throw new IllegalArgumentException(Integer.toString(species.bitSize()));
631 }
632 }
633
634 /**
635 * Returns a shuffle where each lane element is set to a given
636 * {@code int} value logically AND'ed by the species length minus one.
637 * <p>
638 * For each shuffle lane, where {@code N} is the shuffle lane index, the
639 * the {@code int} value at index {@code N} logically AND'ed by
640 * {@code species.length() - 1} is placed into the resulting shuffle at
641 * lane index {@code N}.
642 *
643 * @param species shuffle species
644 * @param ixs the given {@code int} values
645 * @return a shuffle where each lane element is set to a given
646 * {@code int} value
647 * @throws IndexOutOfBoundsException if the number of int values is
648 * {@code < species.length()}
649 */
650 @ForceInline
651 public static Shuffle<Double> shuffleFromValues(Species<Double> species, int... ixs) {
652 if (species.boxType() == DoubleMaxVector.class)
653 return new DoubleMaxVector.DoubleMaxShuffle(ixs);
654 switch (species.bitSize()) {
655 case 64: return new Double64Vector.Double64Shuffle(ixs);
656 case 128: return new Double128Vector.Double128Shuffle(ixs);
657 case 256: return new Double256Vector.Double256Shuffle(ixs);
658 case 512: return new Double512Vector.Double512Shuffle(ixs);
659 default: throw new IllegalArgumentException(Integer.toString(species.bitSize()));
660 }
661 }
662
663 /**
664 * Loads a shuffle from an {@code int} array starting at an offset.
665 * <p>
666 * For each shuffle lane, where {@code N} is the shuffle lane index, the
667 * array element at index {@code i + N} logically AND'ed by
668 * {@code species.length() - 1} is placed into the resulting shuffle at lane
669 * index {@code N}.
670 *
671 * @param species shuffle species
672 * @param ixs the {@code int} array
673 * @param i the offset into the array
674 * @return a shuffle loaded from the {@code int} array
675 * @throws IndexOutOfBoundsException if {@code i < 0}, or
676 * {@code i > a.length - species.length()}
677 */
678 @ForceInline
679 public static Shuffle<Double> shuffleFromArray(Species<Double> species, int[] ixs, int i) {
680 if (species.boxType() == DoubleMaxVector.class)
681 return new DoubleMaxVector.DoubleMaxShuffle(ixs, i);
682 switch (species.bitSize()) {
683 case 64: return new Double64Vector.Double64Shuffle(ixs, i);
684 case 128: return new Double128Vector.Double128Shuffle(ixs, i);
685 case 256: return new Double256Vector.Double256Shuffle(ixs, i);
686 case 512: return new Double512Vector.Double512Shuffle(ixs, i);
687 default: throw new IllegalArgumentException(Integer.toString(species.bitSize()));
688 }
689 }
690
691 // Ops
692
693 @Override
694 public abstract DoubleVector add(Vector<Double> v);
695
696 /**
697 * Adds this vector to the broadcast of an input scalar.
698 * <p>
699 * This is a vector binary operation where the primitive addition operation
700 * ({@code +}) is applied to lane elements.
701 *
702 * @param s the input scalar
703 * @return the result of adding this vector to the broadcast of an input
704 * scalar
705 */
706 public abstract DoubleVector add(double s);
707
708 @Override
709 public abstract DoubleVector add(Vector<Double> v, Mask<Double> m);
710
711 /**
712 * Adds this vector to broadcast of an input scalar,
713 * selecting lane elements controlled by a mask.
714 * <p>
715 * This is a vector binary operation where the primitive addition operation
716 * ({@code +}) is applied to lane elements.
717 *
718 * @param s the input scalar
719 * @param m the mask controlling lane selection
720 * @return the result of adding this vector to the broadcast of an input
721 * scalar
722 */
723 public abstract DoubleVector add(double s, Mask<Double> m);
724
725 @Override
726 public abstract DoubleVector sub(Vector<Double> v);
727
728 /**
729 * Subtracts the broadcast of an input scalar from this vector.
730 * <p>
731 * This is a vector binary operation where the primitive subtraction
732 * operation ({@code -}) is applied to lane elements.
733 *
734 * @param s the input scalar
735 * @return the result of subtracting the broadcast of an input
736 * scalar from this vector
737 */
738 public abstract DoubleVector sub(double s);
739
740 @Override
741 public abstract DoubleVector sub(Vector<Double> v, Mask<Double> m);
742
743 /**
744 * Subtracts the broadcast of an input scalar from this vector, selecting
745 * lane elements controlled by a mask.
746 * <p>
747 * This is a vector binary operation where the primitive subtraction
748 * operation ({@code -}) is applied to lane elements.
749 *
750 * @param s the input scalar
751 * @param m the mask controlling lane selection
752 * @return the result of subtracting the broadcast of an input
753 * scalar from this vector
754 */
755 public abstract DoubleVector sub(double s, Mask<Double> m);
756
757 @Override
758 public abstract DoubleVector mul(Vector<Double> v);
759
760 /**
761 * Multiplies this vector with the broadcast of an input scalar.
762 * <p>
763 * This is a vector binary operation where the primitive multiplication
764 * operation ({@code *}) is applied to lane elements.
765 *
766 * @param s the input scalar
767 * @return the result of multiplying this vector with the broadcast of an
768 * input scalar
769 */
770 public abstract DoubleVector mul(double s);
771
772 @Override
773 public abstract DoubleVector mul(Vector<Double> v, Mask<Double> m);
774
775 /**
776 * Multiplies this vector with the broadcast of an input scalar, selecting
777 * lane elements controlled by a mask.
778 * <p>
779 * This is a vector binary operation where the primitive multiplication
780 * operation ({@code *}) is applied to lane elements.
781 *
782 * @param s the input scalar
783 * @param m the mask controlling lane selection
784 * @return the result of multiplying this vector with the broadcast of an
785 * input scalar
786 */
787 public abstract DoubleVector mul(double s, Mask<Double> m);
788
789 @Override
790 public abstract DoubleVector neg();
791
792 @Override
793 public abstract DoubleVector neg(Mask<Double> m);
794
795 @Override
796 public abstract DoubleVector abs();
797
798 @Override
799 public abstract DoubleVector abs(Mask<Double> m);
800
801 @Override
802 public abstract DoubleVector min(Vector<Double> v);
803
804 @Override
805 public abstract DoubleVector min(Vector<Double> v, Mask<Double> m);
806
807 /**
808 * Returns the minimum of this vector and the broadcast of an input scalar.
809 * <p>
810 * This is a vector binary operation where the operation
811 * {@code (a, b) -> Math.min(a, b)} is applied to lane elements.
812 *
813 * @param s the input scalar
814 * @return the minimum of this vector and the broadcast of an input scalar
815 */
816 public abstract DoubleVector min(double s);
817
818 @Override
819 public abstract DoubleVector max(Vector<Double> v);
820
821 @Override
822 public abstract DoubleVector max(Vector<Double> v, Mask<Double> m);
823
824 /**
825 * Returns the maximum of this vector and the broadcast of an input scalar.
826 * <p>
827 * This is a vector binary operation where the operation
828 * {@code (a, b) -> Math.max(a, b)} is applied to lane elements.
829 *
830 * @param s the input scalar
831 * @return the maximum of this vector and the broadcast of an input scalar
832 */
833 public abstract DoubleVector max(double s);
834
835 @Override
836 public abstract Mask<Double> equal(Vector<Double> v);
837
838 /**
839 * Tests if this vector is equal to the broadcast of an input scalar.
840 * <p>
841 * This is a vector binary test operation where the primitive equals
842 * operation ({@code ==}) is applied to lane elements.
843 *
844 * @param s the input scalar
845 * @return the result mask of testing if this vector is equal to the
846 * broadcast of an input scalar
847 */
848 public abstract Mask<Double> equal(double s);
849
850 @Override
851 public abstract Mask<Double> notEqual(Vector<Double> v);
852
853 /**
854 * Tests if this vector is not equal to the broadcast of an input scalar.
855 * <p>
856 * This is a vector binary test operation where the primitive not equals
857 * operation ({@code !=}) is applied to lane elements.
858 *
859 * @param s the input scalar
860 * @return the result mask of testing if this vector is not equal to the
861 * broadcast of an input scalar
862 */
863 public abstract Mask<Double> notEqual(double s);
864
865 @Override
866 public abstract Mask<Double> lessThan(Vector<Double> v);
867
868 /**
869 * Tests if this vector is less than the broadcast of an input scalar.
870 * <p>
871 * This is a vector binary test operation where the primitive less than
872 * operation ({@code <}) is applied to lane elements.
873 *
874 * @param s the input scalar
875 * @return the mask result of testing if this vector is less than the
876 * broadcast of an input scalar
877 */
878 public abstract Mask<Double> lessThan(double s);
879
880 @Override
881 public abstract Mask<Double> lessThanEq(Vector<Double> v);
882
883 /**
884 * Tests if this vector is less or equal to the broadcast of an input scalar.
885 * <p>
886 * This is a vector binary test operation where the primitive less than
887 * or equal to operation ({@code <=}) is applied to lane elements.
888 *
889 * @param s the input scalar
890 * @return the mask result of testing if this vector is less than or equal
891 * to the broadcast of an input scalar
892 */
893 public abstract Mask<Double> lessThanEq(double s);
894
895 @Override
896 public abstract Mask<Double> greaterThan(Vector<Double> v);
897
898 /**
899 * Tests if this vector is greater than the broadcast of an input scalar.
900 * <p>
901 * This is a vector binary test operation where the primitive greater than
902 * operation ({@code >}) is applied to lane elements.
903 *
904 * @param s the input scalar
905 * @return the mask result of testing if this vector is greater than the
906 * broadcast of an input scalar
907 */
908 public abstract Mask<Double> greaterThan(double s);
909
910 @Override
911 public abstract Mask<Double> greaterThanEq(Vector<Double> v);
912
913 /**
914 * Tests if this vector is greater than or equal to the broadcast of an
915 * input scalar.
916 * <p>
917 * This is a vector binary test operation where the primitive greater than
918 * or equal to operation ({@code >=}) is applied to lane elements.
919 *
920 * @param s the input scalar
921 * @return the mask result of testing if this vector is greater than or
922 * equal to the broadcast of an input scalar
923 */
924 public abstract Mask<Double> greaterThanEq(double s);
925
926 @Override
927 public abstract DoubleVector blend(Vector<Double> v, Mask<Double> m);
928
929 /**
930 * Blends the lane elements of this vector with those of the broadcast of an
931 * input scalar, selecting lanes controlled by a mask.
932 * <p>
933 * For each lane of the mask, at lane index {@code N}, if the mask lane
934 * is set then the lane element at {@code N} from the input vector is
935 * selected and placed into the resulting vector at {@code N},
936 * otherwise the the lane element at {@code N} from this input vector is
937 * selected and placed into the resulting vector at {@code N}.
938 *
939 * @param s the input scalar
940 * @param m the mask controlling lane selection
941 * @return the result of blending the lane elements of this vector with
942 * those of the broadcast of an input scalar
943 */
944 public abstract DoubleVector blend(double s, Mask<Double> m);
945
946 @Override
947 public abstract DoubleVector rearrange(Vector<Double> v,
948 Shuffle<Double> s, Mask<Double> m);
949
950 @Override
951 public abstract DoubleVector rearrange(Shuffle<Double> m);
952
953 @Override
954 public abstract DoubleVector reshape(Species<Double> s);
955
956 @Override
957 public abstract DoubleVector rotateEL(int i);
958
959 @Override
960 public abstract DoubleVector rotateER(int i);
961
962 @Override
963 public abstract DoubleVector shiftEL(int i);
964
965 @Override
966 public abstract DoubleVector shiftER(int i);
967
968 /**
969 * Divides this vector by an input vector.
970 * <p>
971 * This is a vector binary operation where the primitive division
972 * operation ({@code /}) is applied to lane elements.
973 *
974 * @param v the input vector
975 * @return the result of dividing this vector by the input vector
976 */
977 public abstract DoubleVector div(Vector<Double> v);
978
979 /**
980 * Divides this vector by the broadcast of an input scalar.
981 * <p>
982 * This is a vector binary operation where the primitive division
983 * operation ({@code /}) is applied to lane elements.
984 *
985 * @param s the input scalar
986 * @return the result of dividing this vector by the broadcast of an input
987 * scalar
988 */
989 public abstract DoubleVector div(double s);
990
991 /**
992 * Divides this vector by an input vector, selecting lane elements
993 * controlled by a mask.
994 * <p>
995 * This is a vector binary operation where the primitive division
996 * operation ({@code /}) is applied to lane elements.
997 *
998 * @param v the input vector
999 * @param m the mask controlling lane selection
1000 * @return the result of dividing this vector by the input vector
1001 */
1002 public abstract DoubleVector div(Vector<Double> v, Mask<Double> m);
1003
1004 /**
1005 * Divides this vector by the broadcast of an input scalar, selecting lane
1006 * elements controlled by a mask.
1007 * <p>
1008 * This is a vector binary operation where the primitive division
1009 * operation ({@code /}) is applied to lane elements.
1010 *
1011 * @param s the input scalar
1012 * @param m the mask controlling lane selection
1013 * @return the result of dividing this vector by the broadcast of an input
1014 * scalar
1015 */
1016 public abstract DoubleVector div(double s, Mask<Double> m);
1017
1018 /**
1019 * Calculates the square root of this vector.
1020 * <p>
1021 * This is a vector unary operation where the {@link Math#sqrt} operation
1022 * is applied to lane elements.
1023 *
1024 * @return the square root of this vector
1025 */
1026 public abstract DoubleVector sqrt();
1027
1028 /**
1029 * Calculates the square root of this vector, selecting lane elements
1030 * controlled by a mask.
1031 * <p>
1032 * This is a vector unary operation where the {@link Math#sqrt} operation
1033 * is applied to lane elements.
1034 *
1035 * @param m the mask controlling lane selection
1036 * @return the square root of this vector
1037 */
1038 public DoubleVector sqrt(Mask<Double> m) {
1039 return uOp(m, (i, a) -> (double) Math.sqrt((double) a));
1040 }
1041
1042 /**
1043 * Calculates the trigonometric tangent of this vector.
1044 * <p>
1045 * This is a vector unary operation with same semantic definition as
1046 * {@link Math#tan} operation applied to lane elements.
1047 * The implementation is not required to return same
1048 * results as {@link Math#tan}, but adheres to rounding, monotonicity,
1049 * and special case semantics as defined in the {@link Math#tan}
1050 * specifications. The computed result will be within 1 ulp of the
1051 * exact result.
1052 *
1053 * @return the tangent of this vector
1054 */
1055 public DoubleVector tan() {
1056 return uOp((i, a) -> (double) Math.tan((double) a));
1057 }
1058
1059 /**
1060 * Calculates the trigonometric tangent of this vector, selecting lane
1061 * elements controlled by a mask.
1062 * <p>
1063 * Semantics for rounding, monotonicity, and special cases are
1064 * described in {@link DoubleVector#tan}
1065 *
1066 * @param m the mask controlling lane selection
1067 * @return the tangent of this vector
1068 */
1069 public DoubleVector tan(Mask<Double> m) {
1070 return uOp(m, (i, a) -> (double) Math.tan((double) a));
1071 }
1072
1073 /**
1074 * Calculates the hyperbolic tangent of this vector.
1075 * <p>
1076 * This is a vector unary operation with same semantic definition as
1077 * {@link Math#tanh} operation applied to lane elements.
1078 * The implementation is not required to return same
1079 * results as {@link Math#tanh}, but adheres to rounding, monotonicity,
1080 * and special case semantics as defined in the {@link Math#tanh}
1081 * specifications. The computed result will be within 2.5 ulps of the
1082 * exact result.
1083 *
1084 * @return the hyperbolic tangent of this vector
1085 */
1086 public DoubleVector tanh() {
1087 return uOp((i, a) -> (double) Math.tanh((double) a));
1088 }
1089
1090 /**
1091 * Calculates the hyperbolic tangent of this vector, selecting lane elements
1092 * controlled by a mask.
1093 * <p>
1094 * Semantics for rounding, monotonicity, and special cases are
1095 * described in {@link DoubleVector#tanh}
1096 *
1097 * @param m the mask controlling lane selection
1098 * @return the hyperbolic tangent of this vector
1099 */
1100 public DoubleVector tanh(Mask<Double> m) {
1101 return uOp(m, (i, a) -> (double) Math.tanh((double) a));
1102 }
1103
1104 /**
1105 * Calculates the trigonometric sine of this vector.
1106 * <p>
1107 * This is a vector unary operation with same semantic definition as
1108 * {@link Math#sin} operation applied to lane elements.
1109 * The implementation is not required to return same
1110 * results as {@link Math#sin}, but adheres to rounding, monotonicity,
1111 * and special case semantics as defined in the {@link Math#sin}
1112 * specifications. The computed result will be within 1 ulp of the
1113 * exact result.
1114 *
1115 * @return the sine of this vector
1116 */
1117 public DoubleVector sin() {
1118 return uOp((i, a) -> (double) Math.sin((double) a));
1119 }
1120
1121 /**
1122 * Calculates the trigonometric sine of this vector, selecting lane elements
1123 * controlled by a mask.
1124 * <p>
1125 * Semantics for rounding, monotonicity, and special cases are
1126 * described in {@link DoubleVector#sin}
1127 *
1128 * @param m the mask controlling lane selection
1129 * @return the sine of this vector
1130 */
1131 public DoubleVector sin(Mask<Double> m) {
1132 return uOp(m, (i, a) -> (double) Math.sin((double) a));
1133 }
1134
1135 /**
1136 * Calculates the hyperbolic sine of this vector.
1137 * <p>
1138 * This is a vector unary operation with same semantic definition as
1139 * {@link Math#sinh} operation applied to lane elements.
1140 * The implementation is not required to return same
1141 * results as {@link Math#sinh}, but adheres to rounding, monotonicity,
1142 * and special case semantics as defined in the {@link Math#sinh}
1143 * specifications. The computed result will be within 2.5 ulps of the
1144 * exact result.
1145 *
1146 * @return the hyperbolic sine of this vector
1147 */
1148 public DoubleVector sinh() {
1149 return uOp((i, a) -> (double) Math.sinh((double) a));
1150 }
1151
1152 /**
1153 * Calculates the hyperbolic sine of this vector, selecting lane elements
1154 * controlled by a mask.
1155 * <p>
1156 * Semantics for rounding, monotonicity, and special cases are
1157 * described in {@link DoubleVector#sinh}
1158 *
1159 * @param m the mask controlling lane selection
1160 * @return the hyperbolic sine of this vector
1161 */
1162 public DoubleVector sinh(Mask<Double> m) {
1163 return uOp(m, (i, a) -> (double) Math.sinh((double) a));
1164 }
1165
1166 /**
1167 * Calculates the trigonometric cosine of this vector.
1168 * <p>
1169 * This is a vector unary operation with same semantic definition as
1170 * {@link Math#cos} operation applied to lane elements.
1171 * The implementation is not required to return same
1172 * results as {@link Math#cos}, but adheres to rounding, monotonicity,
1173 * and special case semantics as defined in the {@link Math#cos}
1174 * specifications. The computed result will be within 1 ulp of the
1175 * exact result.
1176 *
1177 * @return the cosine of this vector
1178 */
1179 public DoubleVector cos() {
1180 return uOp((i, a) -> (double) Math.cos((double) a));
1181 }
1182
1183 /**
1184 * Calculates the trigonometric cosine of this vector, selecting lane
1185 * elements controlled by a mask.
1186 * <p>
1187 * Semantics for rounding, monotonicity, and special cases are
1188 * described in {@link DoubleVector#cos}
1189 *
1190 * @param m the mask controlling lane selection
1191 * @return the cosine of this vector
1192 */
1193 public DoubleVector cos(Mask<Double> m) {
1194 return uOp(m, (i, a) -> (double) Math.cos((double) a));
1195 }
1196
1197 /**
1198 * Calculates the hyperbolic cosine of this vector.
1199 * <p>
1200 * This is a vector unary operation with same semantic definition as
1201 * {@link Math#cosh} operation applied to lane elements.
1202 * The implementation is not required to return same
1203 * results as {@link Math#cosh}, but adheres to rounding, monotonicity,
1204 * and special case semantics as defined in the {@link Math#cosh}
1205 * specifications. The computed result will be within 2.5 ulps of the
1206 * exact result.
1207 *
1208 * @return the hyperbolic cosine of this vector
1209 */
1210 public DoubleVector cosh() {
1211 return uOp((i, a) -> (double) Math.cosh((double) a));
1212 }
1213
1214 /**
1215 * Calculates the hyperbolic cosine of this vector, selecting lane elements
1216 * controlled by a mask.
1217 * <p>
1218 * Semantics for rounding, monotonicity, and special cases are
1219 * described in {@link DoubleVector#cosh}
1220 *
1221 * @param m the mask controlling lane selection
1222 * @return the hyperbolic cosine of this vector
1223 */
1224 public DoubleVector cosh(Mask<Double> m) {
1225 return uOp(m, (i, a) -> (double) Math.cosh((double) a));
1226 }
1227
1228 /**
1229 * Calculates the arc sine of this vector.
1230 * <p>
1231 * This is a vector unary operation with same semantic definition as
1232 * {@link Math#asin} operation applied to lane elements.
1233 * The implementation is not required to return same
1234 * results as {@link Math#asin}, but adheres to rounding, monotonicity,
1235 * and special case semantics as defined in the {@link Math#asin}
1236 * specifications. The computed result will be within 1 ulp of the
1237 * exact result.
1238 *
1239 * @return the arc sine of this vector
1240 */
1241 public DoubleVector asin() {
1242 return uOp((i, a) -> (double) Math.asin((double) a));
1243 }
1244
1245 /**
1246 * Calculates the arc sine of this vector, selecting lane elements
1247 * controlled by a mask.
1248 * <p>
1249 * Semantics for rounding, monotonicity, and special cases are
1250 * described in {@link DoubleVector#asin}
1251 *
1252 * @param m the mask controlling lane selection
1253 * @return the arc sine of this vector
1254 */
1255 public DoubleVector asin(Mask<Double> m) {
1256 return uOp(m, (i, a) -> (double) Math.asin((double) a));
1257 }
1258
1259 /**
1260 * Calculates the arc cosine of this vector.
1261 * <p>
1262 * This is a vector unary operation with same semantic definition as
1263 * {@link Math#acos} operation applied to lane elements.
1264 * The implementation is not required to return same
1265 * results as {@link Math#acos}, but adheres to rounding, monotonicity,
1266 * and special case semantics as defined in the {@link Math#acos}
1267 * specifications. The computed result will be within 1 ulp of the
1268 * exact result.
1269 *
1270 * @return the arc cosine of this vector
1271 */
1272 public DoubleVector acos() {
1273 return uOp((i, a) -> (double) Math.acos((double) a));
1274 }
1275
1276 /**
1277 * Calculates the arc cosine of this vector, selecting lane elements
1278 * controlled by a mask.
1279 * <p>
1280 * Semantics for rounding, monotonicity, and special cases are
1281 * described in {@link DoubleVector#acos}
1282 *
1283 * @param m the mask controlling lane selection
1284 * @return the arc cosine of this vector
1285 */
1286 public DoubleVector acos(Mask<Double> m) {
1287 return uOp(m, (i, a) -> (double) Math.acos((double) a));
1288 }
1289
1290 /**
1291 * Calculates the arc tangent of this vector.
1292 * <p>
1293 * This is a vector unary operation with same semantic definition as
1294 * {@link Math#atan} operation applied to lane elements.
1295 * The implementation is not required to return same
1296 * results as {@link Math#atan}, but adheres to rounding, monotonicity,
1297 * and special case semantics as defined in the {@link Math#atan}
1298 * specifications. The computed result will be within 1 ulp of the
1299 * exact result.
1300 *
1301 * @return the arc tangent of this vector
1302 */
1303 public DoubleVector atan() {
1304 return uOp((i, a) -> (double) Math.atan((double) a));
1305 }
1306
1307 /**
1308 * Calculates the arc tangent of this vector, selecting lane elements
1309 * controlled by a mask.
1310 * <p>
1311 * Semantics for rounding, monotonicity, and special cases are
1312 * described in {@link DoubleVector#atan}
1313 *
1314 * @param m the mask controlling lane selection
1315 * @return the arc tangent of this vector
1316 */
1317 public DoubleVector atan(Mask<Double> m) {
1318 return uOp(m, (i, a) -> (double) Math.atan((double) a));
1319 }
1320
1321 /**
1322 * Calculates the arc tangent of this vector divided by an input vector.
1323 * <p>
1324 * This is a vector binary operation with same semantic definition as
1325 * {@link Math#atan2} operation applied to lane elements.
1326 * The implementation is not required to return same
1327 * results as {@link Math#atan2}, but adheres to rounding, monotonicity,
1328 * and special case semantics as defined in the {@link Math#atan2}
1329 * specifications. The computed result will be within 2 ulps of the
1330 * exact result.
1331 *
1332 * @param v the input vector
1333 * @return the arc tangent of this vector divided by the input vector
1334 */
1335 public DoubleVector atan2(Vector<Double> v) {
1336 return bOp(v, (i, a, b) -> (double) Math.atan2((double) a, (double) b));
1337 }
1338
1339 /**
1340 * Calculates the arc tangent of this vector divided by the broadcast of an
1341 * an input scalar.
1342 * <p>
1343 * This is a vector binary operation with same semantic definition as
1344 * {@link Math#atan2} operation applied to lane elements.
1345 * The implementation is not required to return same
1346 * results as {@link Math#atan2}, but adheres to rounding, monotonicity,
1347 * and special case semantics as defined in the {@link Math#atan2}
1348 * specifications. The computed result will be within 1 ulp of the
1349 * exact result.
1350 *
1351 * @param s the input scalar
1352 * @return the arc tangent of this vector over the input vector
1353 */
1354 public abstract DoubleVector atan2(double s);
1355
1356 /**
1357 * Calculates the arc tangent of this vector divided by an input vector,
1358 * selecting lane elements controlled by a mask.
1359 * <p>
1360 * Semantics for rounding, monotonicity, and special cases are
1361 * described in {@link DoubleVector#atan2}
1362 *
1363 * @param v the input vector
1364 * @param m the mask controlling lane selection
1365 * @return the arc tangent of this vector divided by the input vector
1366 */
1367 public DoubleVector atan2(Vector<Double> v, Mask<Double> m) {
1368 return bOp(v, m, (i, a, b) -> (double) Math.atan2((double) a, (double) b));
1369 }
1370
1371 /**
1372 * Calculates the arc tangent of this vector divided by the broadcast of an
1373 * an input scalar, selecting lane elements controlled by a mask.
1374 * <p>
1375 * Semantics for rounding, monotonicity, and special cases are
1376 * described in {@link DoubleVector#atan2}
1377 *
1378 * @param s the input scalar
1379 * @param m the mask controlling lane selection
1380 * @return the arc tangent of this vector over the input vector
1381 */
1382 public abstract DoubleVector atan2(double s, Mask<Double> m);
1383
1384 /**
1385 * Calculates the cube root of this vector.
1386 * <p>
1387 * This is a vector unary operation with same semantic definition as
1388 * {@link Math#cbrt} operation applied to lane elements.
1389 * The implementation is not required to return same
1390 * results as {@link Math#cbrt}, but adheres to rounding, monotonicity,
1391 * and special case semantics as defined in the {@link Math#cbrt}
1392 * specifications. The computed result will be within 1 ulp of the
1393 * exact result.
1394 *
1395 * @return the cube root of this vector
1396 */
1397 public DoubleVector cbrt() {
1398 return uOp((i, a) -> (double) Math.cbrt((double) a));
1399 }
1400
1401 /**
1402 * Calculates the cube root of this vector, selecting lane elements
1403 * controlled by a mask.
1404 * <p>
1405 * Semantics for rounding, monotonicity, and special cases are
1406 * described in {@link DoubleVector#cbrt}
1407 *
1408 * @param m the mask controlling lane selection
1409 * @return the cube root of this vector
1410 */
1411 public DoubleVector cbrt(Mask<Double> m) {
1412 return uOp(m, (i, a) -> (double) Math.cbrt((double) a));
1413 }
1414
1415 /**
1416 * Calculates the natural logarithm of this vector.
1417 * <p>
1418 * This is a vector unary operation with same semantic definition as
1419 * {@link Math#log} operation applied to lane elements.
1420 * The implementation is not required to return same
1421 * results as {@link Math#log}, but adheres to rounding, monotonicity,
1422 * and special case semantics as defined in the {@link Math#log}
1423 * specifications. The computed result will be within 1 ulp of the
1424 * exact result.
1425 *
1426 * @return the natural logarithm of this vector
1427 */
1428 public DoubleVector log() {
1429 return uOp((i, a) -> (double) Math.log((double) a));
1430 }
1431
1432 /**
1433 * Calculates the natural logarithm of this vector, selecting lane elements
1434 * controlled by a mask.
1435 * <p>
1436 * Semantics for rounding, monotonicity, and special cases are
1437 * described in {@link DoubleVector#log}
1438 *
1439 * @param m the mask controlling lane selection
1440 * @return the natural logarithm of this vector
1441 */
1442 public DoubleVector log(Mask<Double> m) {
1443 return uOp(m, (i, a) -> (double) Math.log((double) a));
1444 }
1445
1446 /**
1447 * Calculates the base 10 logarithm of this vector.
1448 * <p>
1449 * This is a vector unary operation with same semantic definition as
1450 * {@link Math#log10} operation applied to lane elements.
1451 * The implementation is not required to return same
1452 * results as {@link Math#log10}, but adheres to rounding, monotonicity,
1453 * and special case semantics as defined in the {@link Math#log10}
1454 * specifications. The computed result will be within 1 ulp of the
1455 * exact result.
1456 *
1457 * @return the base 10 logarithm of this vector
1458 */
1459 public DoubleVector log10() {
1460 return uOp((i, a) -> (double) Math.log10((double) a));
1461 }
1462
1463 /**
1464 * Calculates the base 10 logarithm of this vector, selecting lane elements
1465 * controlled by a mask.
1466 * <p>
1467 * Semantics for rounding, monotonicity, and special cases are
1468 * described in {@link DoubleVector#log10}
1469 *
1470 * @param m the mask controlling lane selection
1471 * @return the base 10 logarithm of this vector
1472 */
1473 public DoubleVector log10(Mask<Double> m) {
1474 return uOp(m, (i, a) -> (double) Math.log10((double) a));
1475 }
1476
1477 /**
1478 * Calculates the natural logarithm of the sum of this vector and the
1479 * broadcast of {@code 1}.
1480 * <p>
1481 * This is a vector unary operation with same semantic definition as
1482 * {@link Math#log1p} operation applied to lane elements.
1483 * The implementation is not required to return same
1484 * results as {@link Math#log1p}, but adheres to rounding, monotonicity,
1485 * and special case semantics as defined in the {@link Math#log1p}
1486 * specifications. The computed result will be within 1 ulp of the
1487 * exact result.
1488 *
1489 * @return the natural logarithm of the sum of this vector and the broadcast
1490 * of {@code 1}
1491 */
1492 public DoubleVector log1p() {
1493 return uOp((i, a) -> (double) Math.log1p((double) a));
1494 }
1495
1496 /**
1497 * Calculates the natural logarithm of the sum of this vector and the
1498 * broadcast of {@code 1}, selecting lane elements controlled by a mask.
1499 * <p>
1500 * Semantics for rounding, monotonicity, and special cases are
1501 * described in {@link DoubleVector#log1p}
1502 *
1503 * @param m the mask controlling lane selection
1504 * @return the natural logarithm of the sum of this vector and the broadcast
1505 * of {@code 1}
1506 */
1507 public DoubleVector log1p(Mask<Double> m) {
1508 return uOp(m, (i, a) -> (double) Math.log1p((double) a));
1509 }
1510
1511 /**
1512 * Calculates this vector raised to the power of an input vector.
1513 * <p>
1514 * This is a vector binary operation with same semantic definition as
1515 * {@link Math#pow} operation applied to lane elements.
1516 * The implementation is not required to return same
1517 * results as {@link Math#pow}, but adheres to rounding, monotonicity,
1518 * and special case semantics as defined in the {@link Math#pow}
1519 * specifications. The computed result will be within 1 ulp of the
1520 * exact result.
1521 *
1522 * @param v the input vector
1523 * @return this vector raised to the power of an input vector
1524 */
1525 public DoubleVector pow(Vector<Double> v) {
1526 return bOp(v, (i, a, b) -> (double) Math.pow((double) a, (double) b));
1527 }
1528
1529 /**
1530 * Calculates this vector raised to the power of the broadcast of an input
1531 * scalar.
1532 * <p>
1533 * This is a vector binary operation with same semantic definition as
1534 * {@link Math#pow} operation applied to lane elements.
1535 * The implementation is not required to return same
1536 * results as {@link Math#pow}, but adheres to rounding, monotonicity,
1537 * and special case semantics as defined in the {@link Math#pow}
1538 * specifications. The computed result will be within 1 ulp of the
1539 * exact result.
1540 *
1541 * @param s the input scalar
1542 * @return this vector raised to the power of the broadcast of an input
1543 * scalar.
1544 */
1545 public abstract DoubleVector pow(double s);
1546
1547 /**
1548 * Calculates this vector raised to the power of an input vector, selecting
1549 * lane elements controlled by a mask.
1550 * <p>
1551 * Semantics for rounding, monotonicity, and special cases are
1552 * described in {@link DoubleVector#pow}
1553 *
1554 * @param v the input vector
1555 * @param m the mask controlling lane selection
1556 * @return this vector raised to the power of an input vector
1557 */
1558 public DoubleVector pow(Vector<Double> v, Mask<Double> m) {
1559 return bOp(v, m, (i, a, b) -> (double) Math.pow((double) a, (double) b));
1560 }
1561
1562 /**
1563 * Calculates this vector raised to the power of the broadcast of an input
1564 * scalar, selecting lane elements controlled by a mask.
1565 * <p>
1566 * Semantics for rounding, monotonicity, and special cases are
1567 * described in {@link DoubleVector#pow}
1568 *
1569 * @param s the input scalar
1570 * @param m the mask controlling lane selection
1571 * @return this vector raised to the power of the broadcast of an input
1572 * scalar.
1573 */
1574 public abstract DoubleVector pow(double s, Mask<Double> m);
1575
1576 /**
1577 * Calculates the broadcast of Euler's number {@code e} raised to the power
1578 * of this vector.
1579 * <p>
1580 * This is a vector unary operation with same semantic definition as
1581 * {@link Math#exp} operation applied to lane elements.
1582 * The implementation is not required to return same
1583 * results as {@link Math#exp}, but adheres to rounding, monotonicity,
1584 * and special case semantics as defined in the {@link Math#exp}
1585 * specifications. The computed result will be within 1 ulp of the
1586 * exact result.
1587 *
1588 * @return the broadcast of Euler's number {@code e} raised to the power of
1589 * this vector
1590 */
1591 public DoubleVector exp() {
1592 return uOp((i, a) -> (double) Math.exp((double) a));
1593 }
1594
1595 /**
1596 * Calculates the broadcast of Euler's number {@code e} raised to the power
1597 * of this vector, selecting lane elements controlled by a mask.
1598 * <p>
1599 * Semantics for rounding, monotonicity, and special cases are
1600 * described in {@link DoubleVector#exp}
1601 *
1602 * @param m the mask controlling lane selection
1603 * @return the broadcast of Euler's number {@code e} raised to the power of
1604 * this vector
1605 */
1606 public DoubleVector exp(Mask<Double> m) {
1607 return uOp(m, (i, a) -> (double) Math.exp((double) a));
1608 }
1609
1610 /**
1611 * Calculates the broadcast of Euler's number {@code e} raised to the power
1612 * of this vector minus the broadcast of {@code -1}.
1613 * More specifically as if the following (ignoring any differences in
1614 * numerical accuracy):
1615 * <pre>{@code
1616 * this.exp().sub(this.species().broadcast(1))
1617 * }</pre>
1618 * <p>
1619 * This is a vector unary operation with same semantic definition as
1620 * {@link Math#expm1} operation applied to lane elements.
1621 * The implementation is not required to return same
1622 * results as {@link Math#expm1}, but adheres to rounding, monotonicity,
1623 * and special case semantics as defined in the {@link Math#expm1}
1624 * specifications. The computed result will be within 1 ulp of the
1625 * exact result.
1626 *
1627 * @return the broadcast of Euler's number {@code e} raised to the power of
1628 * this vector minus the broadcast of {@code -1}
1629 */
1630 public DoubleVector expm1() {
1631 return uOp((i, a) -> (double) Math.expm1((double) a));
1632 }
1633
1634 /**
1635 * Calculates the broadcast of Euler's number {@code e} raised to the power
1636 * of this vector minus the broadcast of {@code -1}, selecting lane elements
1637 * controlled by a mask
1638 * More specifically as if the following (ignoring any differences in
1639 * numerical accuracy):
1640 * <pre>{@code
1641 * this.exp(m).sub(this.species().broadcast(1), m)
1642 * }</pre>
1643 * <p>
1644 * Semantics for rounding, monotonicity, and special cases are
1645 * described in {@link DoubleVector#expm1}
1646 *
1647 * @param m the mask controlling lane selection
1648 * @return the broadcast of Euler's number {@code e} raised to the power of
1649 * this vector minus the broadcast of {@code -1}
1650 */
1651 public DoubleVector expm1(Mask<Double> m) {
1652 return uOp(m, (i, a) -> (double) Math.expm1((double) a));
1653 }
1654
1655 /**
1656 * Calculates the product of this vector and a first input vector summed
1657 * with a second input vector.
1658 * More specifically as if the following (ignoring any differences in
1659 * numerical accuracy):
1660 * <pre>{@code
1661 * this.mul(v1).add(v2)
1662 * }</pre>
1663 * <p>
1664 * This is a vector ternary operation where the {@link Math#fma} operation
1665 * is applied to lane elements.
1666 *
1667 * @param v1 the first input vector
1668 * @param v2 the second input vector
1669 * @return the product of this vector and the first input vector summed with
1670 * the second input vector
1671 */
1672 public abstract DoubleVector fma(Vector<Double> v1, Vector<Double> v2);
1673
1674 /**
1675 * Calculates the product of this vector and the broadcast of a first input
1676 * scalar summed with the broadcast of a second input scalar.
1677 * More specifically as if the following:
1678 * <pre>{@code
1679 * this.fma(this.species().broadcast(s1), this.species().broadcast(s2))
1680 * }</pre>
1681 * <p>
1682 * This is a vector ternary operation where the {@link Math#fma} operation
1683 * is applied to lane elements.
1684 *
1685 * @param s1 the first input scalar
1686 * @param s2 the second input scalar
1687 * @return the product of this vector and the broadcast of a first input
1688 * scalar summed with the broadcast of a second input scalar
1689 */
1690 public abstract DoubleVector fma(double s1, double s2);
1691
1692 /**
1693 * Calculates the product of this vector and a first input vector summed
1694 * with a second input vector, selecting lane elements controlled by a mask.
1695 * More specifically as if the following (ignoring any differences in
1696 * numerical accuracy):
1697 * <pre>{@code
1698 * this.mul(v1, m).add(v2, m)
1699 * }</pre>
1700 * <p>
1701 * This is a vector ternary operation where the {@link Math#fma} operation
1702 * is applied to lane elements.
1703 *
1704 * @param v1 the first input vector
1705 * @param v2 the second input vector
1706 * @param m the mask controlling lane selection
1707 * @return the product of this vector and the first input vector summed with
1708 * the second input vector
1709 */
1710 public DoubleVector fma(Vector<Double> v1, Vector<Double> v2, Mask<Double> m) {
1711 return tOp(v1, v2, m, (i, a, b, c) -> Math.fma(a, b, c));
1712 }
1713
1714 /**
1715 * Calculates the product of this vector and the broadcast of a first input
1716 * scalar summed with the broadcast of a second input scalar, selecting lane
1717 * elements controlled by a mask
1718 * More specifically as if the following:
1719 * <pre>{@code
1720 * this.fma(this.species().broadcast(s1), this.species().broadcast(s2), m)
1721 * }</pre>
1722 * <p>
1723 * This is a vector ternary operation where the {@link Math#fma} operation
1724 * is applied to lane elements.
1725 *
1726 * @param s1 the first input scalar
1727 * @param s2 the second input scalar
1728 * @param m the mask controlling lane selection
1729 * @return the product of this vector and the broadcast of a first input
1730 * scalar summed with the broadcast of a second input scalar
1731 */
1732 public abstract DoubleVector fma(double s1, double s2, Mask<Double> m);
1733
1734 /**
1735 * Calculates square root of the sum of the squares of this vector and an
1736 * input vector.
1737 * More specifically as if the following (ignoring any differences in
1738 * numerical accuracy):
1739 * <pre>{@code
1740 * this.mul(this).add(v.mul(v)).sqrt()
1741 * }</pre>
1742 * <p>
1743 * This is a vector binary operation with same semantic definition as
1744 * {@link Math#hypot} operation applied to lane elements.
1745 * The implementation is not required to return same
1746 * results as {@link Math#hypot}, but adheres to rounding, monotonicity,
1747 * and special case semantics as defined in the {@link Math#hypot}
1748 * specifications. The computed result will be within 1 ulp of the
1749 * exact result.
1750 *
1751 * @param v the input vector
1752 * @return square root of the sum of the squares of this vector and an input
1753 * vector
1754 */
1755 public DoubleVector hypot(Vector<Double> v) {
1756 return bOp(v, (i, a, b) -> (double) Math.hypot((double) a, (double) b));
1757 }
1758
1759 /**
1760 * Calculates square root of the sum of the squares of this vector and the
1761 * broadcast of an input scalar.
1762 * More specifically as if the following (ignoring any differences in
1763 * numerical accuracy):
1764 * <pre>{@code
1765 * this.mul(this).add(this.species().broadcast(v * v)).sqrt()
1766 * }</pre>
1767 * <p>
1768 * This is a vector binary operation with same semantic definition as
1769 * {@link Math#hypot} operation applied to lane elements.
1770 * The implementation is not required to return same
1771 * results as {@link Math#hypot}, but adheres to rounding, monotonicity,
1772 * and special case semantics as defined in the {@link Math#hypot}
1773 * specifications. The computed result will be within 1 ulp of the
1774 * exact result.
1775 *
1776 * @param s the input scalar
1777 * @return square root of the sum of the squares of this vector and the
1778 * broadcast of an input scalar
1779 */
1780 public abstract DoubleVector hypot(double s);
1781
1782 /**
1783 * Calculates square root of the sum of the squares of this vector and an
1784 * input vector, selecting lane elements controlled by a mask.
1785 * More specifically as if the following (ignoring any differences in
1786 * numerical accuracy):
1787 * <pre>{@code
1788 * this.mul(this, m).add(v.mul(v), m).sqrt(m)
1789 * }</pre>
1790 * <p>
1791 * Semantics for rounding, monotonicity, and special cases are
1792 * described in {@link DoubleVector#hypot}
1793 *
1794 * @param v the input vector
1795 * @param m the mask controlling lane selection
1796 * @return square root of the sum of the squares of this vector and an input
1797 * vector
1798 */
1799 public DoubleVector hypot(Vector<Double> v, Mask<Double> m) {
1800 return bOp(v, m, (i, a, b) -> (double) Math.hypot((double) a, (double) b));
1801 }
1802
1803 /**
1804 * Calculates square root of the sum of the squares of this vector and the
1805 * broadcast of an input scalar, selecting lane elements controlled by a
1806 * mask.
1807 * More specifically as if the following (ignoring any differences in
1808 * numerical accuracy):
1809 * <pre>{@code
1810 * this.mul(this, m).add(this.species().broadcast(v * v), m).sqrt(m)
1811 * }</pre>
1812 * <p>
1813 * Semantics for rounding, monotonicity, and special cases are
1814 * described in {@link DoubleVector#hypot}
1815 *
1816 * @param s the input scalar
1817 * @param m the mask controlling lane selection
1818 * @return square root of the sum of the squares of this vector and the
1819 * broadcast of an input scalar
1820 */
1821 public abstract DoubleVector hypot(double s, Mask<Double> m);
1822
1823
1824 @Override
1825 public abstract void intoByteArray(byte[] a, int ix);
1826
1827 @Override
1828 public abstract void intoByteArray(byte[] a, int ix, Mask<Double> m);
1829
1830 @Override
1831 public abstract void intoByteBuffer(ByteBuffer bb, int ix);
1832
1833 @Override
1834 public abstract void intoByteBuffer(ByteBuffer bb, int ix, Mask<Double> m);
1835
1836
1837 // Type specific horizontal reductions
1838 /**
1839 * Adds all lane elements of this vector.
1840 * <p>
1841 * This is a vector reduction operation where the addition
1842 * operation ({@code +}) is applied to lane elements,
1843 * and the identity value is {@code 0.0}.
1844 *
1845 * <p>The value of a floating-point sum is a function both of the input values as well
1846 * as the order of addition operations. The order of addition operations of this method
1847 * is intentionally not defined to allow for JVM to generate optimal machine
1848 * code for the underlying platform at runtime. If the platform supports a vector
1849 * instruction to add all values in the vector, or if there is some other efficient machine
1850 * code sequence, then the JVM has the option of generating this machine code. Otherwise,
1851 * the default implementation of adding vectors sequentially from left to right is used.
1852 * For this reason, the output of this method may vary for the same input values.
1853 *
1854 * @return the addition of all the lane elements of this vector
1855 */
1856 public abstract double addAll();
1857
1858 /**
1859 * Adds all lane elements of this vector, selecting lane elements
1860 * controlled by a mask.
1861 * <p>
1862 * This is a vector reduction operation where the addition
1863 * operation ({@code +}) is applied to lane elements,
1864 * and the identity value is {@code 0.0}.
1865 *
1866 * <p>The value of a floating-point sum is a function both of the input values as well
1867 * as the order of addition operations. The order of addition operations of this method
1868 * is intentionally not defined to allow for JVM to generate optimal machine
1869 * code for the underlying platform at runtime. If the platform supports a vector
1870 * instruction to add all values in the vector, or if there is some other efficient machine
1871 * code sequence, then the JVM has the option of generating this machine code. Otherwise,
1872 * the default implementation of adding vectors sequentially from left to right is used.
1873 * For this reason, the output of this method may vary on the same input values.
1874 *
1875 * @param m the mask controlling lane selection
1876 * @return the addition of the selected lane elements of this vector
1877 */
1878 public abstract double addAll(Mask<Double> m);
1879
1880 /**
1881 * Multiplies all lane elements of this vector.
1882 * <p>
1883 * This is a vector reduction operation where the
1884 * multiplication operation ({@code *}) is applied to lane elements,
1885 * and the identity value is {@code 1.0}.
1886 *
1887 * <p>The order of multiplication operations of this method
1888 * is intentionally not defined to allow for JVM to generate optimal machine
1889 * code for the underlying platform at runtime. If the platform supports a vector
1890 * instruction to multiply all values in the vector, or if there is some other efficient machine
1891 * code sequence, then the JVM has the option of generating this machine code. Otherwise,
1892 * the default implementation of multiplying vectors sequentially from left to right is used.
1893 * For this reason, the output of this method may vary on the same input values.
1894 *
1895 * @return the multiplication of all the lane elements of this vector
1896 */
1897 public abstract double mulAll();
1898
1899 /**
1900 * Multiplies all lane elements of this vector, selecting lane elements
1901 * controlled by a mask.
1902 * <p>
1903 * This is a vector reduction operation where the
1904 * multiplication operation ({@code *}) is applied to lane elements,
1905 * and the identity value is {@code 1.0}.
1906 *
1907 * <p>The order of multiplication operations of this method
1908 * is intentionally not defined to allow for JVM to generate optimal machine
1909 * code for the underlying platform at runtime. If the platform supports a vector
1910 * instruction to multiply all values in the vector, or if there is some other efficient machine
1911 * code sequence, then the JVM has the option of generating this machine code. Otherwise,
1912 * the default implementation of multiplying vectors sequentially from left to right is used.
1913 * For this reason, the output of this method may vary on the same input values.
1914 *
1915 * @param m the mask controlling lane selection
1916 * @return the multiplication of all the lane elements of this vector
1917 */
1918 public abstract double mulAll(Mask<Double> m);
1919
1920 /**
1921 * Returns the minimum lane element of this vector.
1922 * <p>
1923 * This is an associative vector reduction operation where the operation
1924 * {@code (a, b) -> Math.min(a, b)} is applied to lane elements,
1925 * and the identity value is
1926 * {@link Double#POSITIVE_INFINITY}.
1927 *
1928 * @return the minimum lane element of this vector
1929 */
1930 public abstract double minAll();
1931
1932 /**
1933 * Returns the minimum lane element of this vector, selecting lane elements
1934 * controlled by a mask.
1935 * <p>
1936 * This is an associative vector reduction operation where the operation
1937 * {@code (a, b) -> Math.min(a, b)} is applied to lane elements,
1938 * and the identity value is
1939 * {@link Double#POSITIVE_INFINITY}.
1940 *
1941 * @param m the mask controlling lane selection
1942 * @return the minimum lane element of this vector
1943 */
1944 public abstract double minAll(Mask<Double> m);
1945
1946 /**
1947 * Returns the maximum lane element of this vector.
1948 * <p>
1949 * This is an associative vector reduction operation where the operation
1950 * {@code (a, b) -> Math.max(a, b)} is applied to lane elements,
1951 * and the identity value is
1952 * {@link Double#NEGATIVE_INFINITY}.
1953 *
1954 * @return the maximum lane element of this vector
1955 */
1956 public abstract double maxAll();
1957
1958 /**
1959 * Returns the maximum lane element of this vector, selecting lane elements
1960 * controlled by a mask.
1961 * <p>
1962 * This is an associative vector reduction operation where the operation
1963 * {@code (a, b) -> Math.max(a, b)} is applied to lane elements,
1964 * and the identity value is
1965 * {@link Double#NEGATIVE_INFINITY}.
1966 *
1967 * @param m the mask controlling lane selection
1968 * @return the maximum lane element of this vector
1969 */
1970 public abstract double maxAll(Mask<Double> m);
1971
1972
1973 // Type specific accessors
1974
1975 /**
1976 * Gets the lane element at lane index {@code i}
1977 *
1978 * @param i the lane index
1979 * @return the lane element at lane index {@code i}
1980 * @throws IllegalArgumentException if the index is is out of range
1981 * ({@code < 0 || >= length()})
1982 */
1983 public abstract double get(int i);
1984
1985 /**
1986 * Replaces the lane element of this vector at lane index {@code i} with
1987 * value {@code e}.
1988 * <p>
1989 * This is a cross-lane operation and behaves as if it returns the result
1990 * of blending this vector with an input vector that is the result of
1991 * broadcasting {@code e} and a mask that has only one lane set at lane
1992 * index {@code i}.
1993 *
1994 * @param i the lane index of the lane element to be replaced
1995 * @param e the value to be placed
1996 * @return the result of replacing the lane element of this vector at lane
1997 * index {@code i} with value {@code e}.
1998 * @throws IllegalArgumentException if the index is is out of range
1999 * ({@code < 0 || >= length()})
2000 */
2001 public abstract DoubleVector with(int i, double e);
2002
2003 // Type specific extractors
2004
2005 /**
2006 * Returns an array containing the lane elements of this vector.
2007 * <p>
2008 * This method behaves as if it {@link #intoArray(double[], int)} stores}
2009 * this vector into an allocated array and returns the array as follows:
2010 * <pre>{@code
2011 * double[] a = new double[this.length()];
2012 * this.intoArray(a, 0);
2013 * return a;
2014 * }</pre>
2015 *
2016 * @return an array containing the the lane elements of this vector
2017 */
2018 @ForceInline
2019 public final double[] toArray() {
2020 double[] a = new double[species().length()];
2021 intoArray(a, 0);
2022 return a;
2023 }
2024
2025 /**
2026 * Stores this vector into an array starting at offset.
2027 * <p>
2028 * For each vector lane, where {@code N} is the vector lane index,
2029 * the lane element at index {@code N} is stored into the array at index
2030 * {@code i + N}.
2031 *
2032 * @param a the array
2033 * @param i the offset into the array
2034 * @throws IndexOutOfBoundsException if {@code i < 0}, or
2035 * {@code i > a.length - this.length()}
2036 */
2037 public abstract void intoArray(double[] a, int i);
2038
2039 /**
2040 * Stores this vector into an array starting at offset and using a mask.
2041 * <p>
2042 * For each vector lane, where {@code N} is the vector lane index,
2043 * if the mask lane at index {@code N} is set then the lane element at
2044 * index {@code N} is stored into the array index {@code i + N}.
2045 *
2046 * @param a the array
2047 * @param i the offset into the array
2048 * @param m the mask
2049 * @throws IndexOutOfBoundsException if {@code i < 0}, or
2050 * for any vector lane index {@code N} where the mask at lane {@code N}
2051 * is set {@code i >= a.length - N}
2052 */
2053 public abstract void intoArray(double[] a, int i, Mask<Double> m);
2054
2055 /**
2056 * Stores this vector into an array using indexes obtained from an index
2057 * map.
2058 * <p>
2059 * For each vector lane, where {@code N} is the vector lane index, the
2060 * lane element at index {@code N} is stored into the array at index
2061 * {@code i + indexMap[j + N]}.
2062 *
2063 * @param a the array
2064 * @param i the offset into the array, may be negative if relative
2065 * indexes in the index map compensate to produce a value within the
2066 * array bounds
2067 * @param indexMap the index map
2068 * @param j the offset into the index map
2069 * @throws IndexOutOfBoundsException if {@code j < 0}, or
2070 * {@code j > indexMap.length - this.length()},
2071 * or for any vector lane index {@code N} the result of
2072 * {@code i + indexMap[j + N]} is {@code < 0} or {@code >= a.length}
2073 */
2074 public abstract void intoArray(double[] a, int i, int[] indexMap, int j);
2075
2076 /**
2077 * Stores this vector into an array using indexes obtained from an index
2078 * map and using a mask.
2079 * <p>
2080 * For each vector lane, where {@code N} is the vector lane index,
2081 * if the mask lane at index {@code N} is set then the lane element at
2082 * index {@code N} is stored into the array at index
2083 * {@code i + indexMap[j + N]}.
2084 *
2085 * @param a the array
2086 * @param i the offset into the array, may be negative if relative
2087 * indexes in the index map compensate to produce a value within the
2088 * array bounds
2089 * @param m the mask
2090 * @param indexMap the index map
2091 * @param j the offset into the index map
2092 * @throws IndexOutOfBoundsException if {@code j < 0}, or
2093 * {@code j > indexMap.length - this.length()},
2094 * or for any vector lane index {@code N} where the mask at lane
2095 * {@code N} is set the result of {@code i + indexMap[j + N]} is
2096 * {@code < 0} or {@code >= a.length}
2097 */
2098 public abstract void intoArray(double[] a, int i, Mask<Double> m, int[] indexMap, int j);
2099 // Species
2100
2101 @Override
2102 public abstract Species<Double> species();
2103
2104 /**
2105 * Class representing {@link DoubleVector}'s of the same {@link Vector.Shape Shape}.
2106 */
2107 static final class DoubleSpecies extends Vector.AbstractSpecies<Double> {
2108 final Function<double[], DoubleVector> vectorFactory;
2109 final Function<boolean[], Vector.Mask<Double>> maskFactory;
2110
2111 private DoubleSpecies(Vector.Shape shape,
2112 Class<?> boxType,
2113 Class<?> maskType,
2114 Function<double[], DoubleVector> vectorFactory,
2115 Function<boolean[], Vector.Mask<Double>> maskFactory) {
2116 super(shape, double.class, Double.SIZE, boxType, maskType);
2117 this.vectorFactory = vectorFactory;
2118 this.maskFactory = maskFactory;
2119 }
2120
2121 interface FOp {
2122 double apply(int i);
2123 }
2124
2125 interface FOpm {
2126 boolean apply(int i);
2127 }
2128
2129 DoubleVector op(FOp f) {
2130 double[] res = new double[length()];
2131 for (int i = 0; i < length(); i++) {
2132 res[i] = f.apply(i);
2133 }
2134 return vectorFactory.apply(res);
2135 }
2136
2137 DoubleVector op(Vector.Mask<Double> o, FOp f) {
2138 double[] res = new double[length()];
2139 boolean[] mbits = ((AbstractMask<Double>)o).getBits();
2140 for (int i = 0; i < length(); i++) {
2141 if (mbits[i]) {
2142 res[i] = f.apply(i);
2143 }
2144 }
2145 return vectorFactory.apply(res);
2146 }
2147
2148 Vector.Mask<Double> opm(IntVector.IntSpecies.FOpm f) {
2149 boolean[] res = new boolean[length()];
2150 for (int i = 0; i < length(); i++) {
2151 res[i] = (boolean)f.apply(i);
2152 }
2153 return maskFactory.apply(res);
2154 }
2155 }
2156
2157 /**
2158 * Finds the preferred species for an element type of {@code double}.
2159 * <p>
2160 * A preferred species is a species chosen by the platform that has a
2161 * shape of maximal bit size. A preferred species for different element
2162 * types will have the same shape, and therefore vectors, masks, and
2163 * shuffles created from such species will be shape compatible.
2164 *
2165 * @return the preferred species for an element type of {@code double}
2166 */
2167 private static DoubleSpecies preferredSpecies() {
2168 return (DoubleSpecies) Species.ofPreferred(double.class);
2169 }
2170
2171 /**
2172 * Finds a species for an element type of {@code double} and shape.
2173 *
2174 * @param s the shape
2175 * @return a species for an element type of {@code double} and shape
2176 * @throws IllegalArgumentException if no such species exists for the shape
2177 */
2178 static DoubleSpecies species(Vector.Shape s) {
2179 Objects.requireNonNull(s);
2180 switch (s) {
2181 case S_64_BIT: return (DoubleSpecies) SPECIES_64;
2182 case S_128_BIT: return (DoubleSpecies) SPECIES_128;
2183 case S_256_BIT: return (DoubleSpecies) SPECIES_256;
2184 case S_512_BIT: return (DoubleSpecies) SPECIES_512;
2185 case S_Max_BIT: return (DoubleSpecies) SPECIES_MAX;
2186 default: throw new IllegalArgumentException("Bad shape: " + s);
2187 }
2188 }
2189
2190 /** Species representing {@link DoubleVector}s of {@link Vector.Shape#S_64_BIT Shape.S_64_BIT}. */
2191 public static final Species<Double> SPECIES_64 = new DoubleSpecies(Shape.S_64_BIT, Double64Vector.class, Double64Vector.Double64Mask.class,
2192 Double64Vector::new, Double64Vector.Double64Mask::new);
2193
2194 /** Species representing {@link DoubleVector}s of {@link Vector.Shape#S_128_BIT Shape.S_128_BIT}. */
2195 public static final Species<Double> SPECIES_128 = new DoubleSpecies(Shape.S_128_BIT, Double128Vector.class, Double128Vector.Double128Mask.class,
2196 Double128Vector::new, Double128Vector.Double128Mask::new);
2197
2198 /** Species representing {@link DoubleVector}s of {@link Vector.Shape#S_256_BIT Shape.S_256_BIT}. */
2199 public static final Species<Double> SPECIES_256 = new DoubleSpecies(Shape.S_256_BIT, Double256Vector.class, Double256Vector.Double256Mask.class,
2200 Double256Vector::new, Double256Vector.Double256Mask::new);
2201
2202 /** Species representing {@link DoubleVector}s of {@link Vector.Shape#S_512_BIT Shape.S_512_BIT}. */
2203 public static final Species<Double> SPECIES_512 = new DoubleSpecies(Shape.S_512_BIT, Double512Vector.class, Double512Vector.Double512Mask.class,
2204 Double512Vector::new, Double512Vector.Double512Mask::new);
2205
2206 /** Species representing {@link DoubleVector}s of {@link Vector.Shape#S_Max_BIT Shape.S_Max_BIT}. */
2207 public static final Species<Double> SPECIES_MAX = new DoubleSpecies(Shape.S_Max_BIT, DoubleMaxVector.class, DoubleMaxVector.DoubleMaxMask.class,
2208 DoubleMaxVector::new, DoubleMaxVector.DoubleMaxMask::new);
2209
2210 /**
2211 * Preferred species for {@link DoubleVector}s.
2212 * A preferred species is a species of maximal bit size for the platform.
2213 */
2214 public static final Species<Double> SPECIES_PREFERRED = (Species<Double>) preferredSpecies();
2215 }