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Support any layout in Matrix.apply(rowRange, colRange) gather - #148

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Quafadas merged 2 commits into
mainfrom
matrixinstance-general-gather
Aug 11, 2026
Merged

Support any layout in Matrix.apply(rowRange, colRange) gather#148
Quafadas merged 2 commits into
mainfrom
matrixinstance-general-gather

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@Quafadas

@Quafadas Quafadas commented Aug 11, 2026

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apply(rowRange, colRange) takes a zero-copy submatrix when both selections are contiguous runs, and otherwise gathers the selected elements into a fresh matrix. That gather was guarded on m.isDenseColMajor with a bare ??? for everything else, so gathering from a row-major, offset or padded matrix threw NotImplementedError.

The guard was buying nothing. Its hardcoded colpos * m.rows + rowPos is exactly what m.layout.linearIndex(rowPos, colpos) evaluates to when rowStride == 1, colStride == rows and offset == 0 — i.e. under precisely the condition it tested — and linearIndex is @thin, so the general form costs nothing over the special case. The two branches therefore collapse into one that is correct for every layout. Same reasoning, and same outcome, as the layout-specific branches already removed from update(row, col, value) and reduceAlongDimension.

Also moved the result array allocation inside the gather branch. It sat above the if, so every zero-copy submatrix call allocated a full result-sized array and immediately discarded it.

Tests: the existing suite covered the gather on exactly one layout — dense column-major, the one case the guard admitted — so the new path had no coverage at all. Added three fixtures that are the same logical matrix as mat1to9 in layouts the guard rejected (dense row-major, a padded colStride, and an offset submatrix view whose backing array holds the parent's other elements, which a mis-derived index would pick up). Every gather shape is asserted against dense column-major as the reference, so the branches must agree rather than merely not throw. Also covers out-of-order and repeated indices — gather semantics the method has always had but nothing exercised — and that the result is a copy rather than a view.

Simon Parten
Support any layout in Matrix.apply(rowRange, colRange) gather
060d4a7 
apply(rowRange, colRange) takes a zero-copy submatrix when both
selections are contiguous runs, and otherwise gathers the selected
elements into a fresh matrix. That gather was guarded on
`m.isDenseColMajor` with a bare `???` for everything else, so gathering
from a row-major, offset or padded matrix threw NotImplementedError.

The guard was buying nothing. Its hardcoded `colpos * m.rows + rowPos`
is exactly what `m.layout.linearIndex(rowPos, colpos)` evaluates to
when rowStride == 1, colStride == rows and offset == 0 — i.e. under
precisely the condition it tested — and linearIndex is @thin, so the
general form costs nothing over the special case. The two branches
therefore collapse into one that is correct for every layout. Same
reasoning, and same outcome, as the layout-specific branches already
removed from update(row, col, value) and reduceAlongDimension.

Also moved the result array allocation inside the gather branch. It sat
above the `if`, so every zero-copy submatrix call allocated a full
result-sized array and immediately discarded it.

Tests: the existing suite covered the gather on exactly one layout —
dense column-major, the one case the guard admitted — so the new path
had no coverage at all. Added three fixtures that are the same logical
matrix as mat1to9 in layouts the guard rejected (dense row-major, a
padded colStride, and an offset submatrix view whose backing array
holds the parent's other elements, which a mis-derived index would pick
up). Every gather shape is asserted against dense column-major as the
reference, so the branches must agree rather than merely not throw.
Also covers out-of-order and repeated indices — gather semantics the
method has always had but nothing exercised — and that the result is a
copy rather than a view.
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github-actions Bot commented Aug 11, 2026 •
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Bytecode audit (Tier 1) — ✅ clean

JDK 25.0.1 (major 25), OpenJDK 64-Bit Server VM

Vector lanes (DoubleVector.SPECIES_PREFERRED.length()): 4

threshold value provenance
MaxTrivialSize 6 discovered
MaxInlineSize 35 discovered
FreqInlineSize 325 discovered
MaxInlineLevel 15 discovered
InlineSmallCode 2500 discovered
NodeCountInliningCutoff 18000 assumed
HugeMethodLimit 8000 assumed

-XX:HugeMethodLimit= was rejected on the command line: a develop flag compiled out of this product build, so 8000 is taken from the HotSpot source and cannot be confirmed against the running JVM.

metric now baseline delta
cheatsheet methods 57 — —
total bytes 36598 51103 -28.4%
distinct library ops 205 178 +15.2%
bytes per op 178.5 287.1 -37.8%

none

Annotated methods (182)
method annotations bytes budget used loop at
vecxt.floatarrays$.$minus$eq @Thin 26 74% of 35 no floatarrays.scala:736
vecxt.doublearrays$.$minus$eq @Thin 26 74% of 35 no doublearrays.scala:1031
vecxt.intarrays$.$plus @Thin 24 69% of 35 no intarrays.scala:546
vecxt.intarrays$.$minus @Thin 24 69% of 35 no intarrays.scala:399
vecxt.floatarrays$.$minus @Thin 24 69% of 35 no floatarrays.scala:725
vecxt.doublearrays$.$minus @Thin 24 69% of 35 no doublearrays.scala:1019
vecxt.floatarrays$.dot @Thin 23 66% of 35 no floatarrays.scala:651
vecxt.doublearrays$.dot @Thin 23 66% of 35 no doublearrays.scala:1004
vecxt.NDArrayFloatOps$.compareGeneral @HotPath 195 60% of 325 yes ndarrayFloatOps.scala:67
vecxt.NDArrayFloatOps$.binaryOpGeneral @HotPath 195 60% of 325 yes ndarrayFloatOps.scala:20
vecxt.NDArrayIntOps$.compareGeneral @HotPath 186 57% of 325 yes ndarrayIntOps.scala:67
vecxt.NDArrayIntOps$.binaryOpGeneral @HotPath 186 57% of 325 yes ndarrayIntOps.scala:19
vecxt.NDArrayDoubleOps$.compareGeneral @HotPath 186 57% of 325 yes ndarrayDoubleOps.scala:74
vecxt.NDArrayDoubleOps$.binaryOpGeneral @HotPath 186 57% of 325 yes ndarrayDoubleOps.scala:24
vecxt.doublearrays$.clamp$bang @AllocFree @HotPath 180 55% of 325 yes doublearrays.scala:898
vecxt.matrix$Layout.linearIndex @Thin 19 54% of 35 no matrix.scala:48
vecxt.floatarrays$.clamp$bang @AllocFree @HotPath 172 53% of 325 yes floatarrays.scala:419
vecxt.NDArrayFloatOps$.compareScalarGeneral @HotPath 165 51% of 325 yes ndarrayFloatOps.scala:94
vecxt.NDArrayFloatOps$.binaryOpInPlaceGeneral @HotPath 162 50% of 325 yes ndarrayFloatOps.scala:119
vecxt.NDArrayDoubleOps$.compareScalarGeneral @HotPath 157 48% of 325 yes ndarrayDoubleOps.scala:102
vecxt.NDArrayIntOps$.compareScalarGeneral @HotPath 156 48% of 325 yes ndarrayIntOps.scala:94
vecxt.NDArrayIntOps$.binaryOpInPlaceGeneral @HotPath 153 47% of 325 yes ndarrayIntOps.scala:119
vecxt.NDArrayDoubleOps$.binaryOpInPlaceGeneral @HotPath 153 47% of 325 yes ndarrayDoubleOps.scala:131
vecxt.NDArrayIntOps$.unaryOpGeneral @HotPath 152 47% of 325 yes ndarrayIntOps.scala:42
vecxt.NDArrayDoubleOps$.unaryOpGeneral @HotPath 152 47% of 325 yes ndarrayDoubleOps.scala:48
vecxt.intarrays$.$minus @Thin 16 46% of 35 no intarrays.scala:519
vecxt.floatarrays$.cumsum @Thin 15 43% of 35 no floatarrays.scala:695
vecxt.doublearrays$.cumsum @Thin 15 43% of 35 no doublearrays.scala:985
vecxt.doublearrays$.fillLinspace @AllocFree @HotPath 133 41% of 325 yes doublearrays.scala:34
vecxt.intarrays$.increments @HotPath 121 37% of 325 yes intarrays.scala:219
vecxt.ndarray$.mkNDArray @Thin 13 37% of 35 no ndarray.scala:188
vecxt.floatarrays$.norm @Thin 13 37% of 35 no floatarrays.scala:656
vecxt.doublearrays$.norm @Thin 13 37% of 35 no doublearrays.scala:1009
vecxt.doublearrays$.$plus @AllocFree @HotPath 116 36% of 325 yes doublearrays.scala:1065
vecxt.doublearrays$.$minus @AllocFree @HotPath 116 36% of 325 yes doublearrays.scala:1128
vecxt.doublearrays$.$div @AllocFree @HotPath 116 36% of 325 yes doublearrays.scala:1341
vecxt.doublearrays$.increments @HotPath 110 34% of 325 yes doublearrays.scala:397
vecxt.intarrays$.dot @AllocFree @HotPath 108 33% of 325 yes intarrays.scala:372
vecxt.doublearrays$.unary_$minus @HotPath 108 33% of 325 yes doublearrays.scala:172
vecxt.doublearrays$.tanh @HotPath 108 33% of 325 yes doublearrays.scala:365
vecxt.doublearrays$.tan @HotPath 108 33% of 325 yes doublearrays.scala:354
vecxt.doublearrays$.sqrt @HotPath 108 33% of 325 yes doublearrays.scala:317
vecxt.doublearrays$.sinh @HotPath 108 33% of 325 yes doublearrays.scala:339
vecxt.doublearrays$.sin @HotPath 108 33% of 325 yes doublearrays.scala:328
vecxt.doublearrays$.log1p @HotPath 108 33% of 325 yes doublearrays.scala:306
vecxt.doublearrays$.log10 @HotPath 108 33% of 325 yes doublearrays.scala:295
vecxt.doublearrays$.log @HotPath 108 33% of 325 yes doublearrays.scala:284
vecxt.doublearrays$.expm1 @HotPath 108 33% of 325 yes doublearrays.scala:273
vecxt.doublearrays$.exp @HotPath 108 33% of 325 yes doublearrays.scala:262
vecxt.doublearrays$.cosh @HotPath 108 33% of 325 yes doublearrays.scala:251
vecxt.doublearrays$.cos @HotPath 108 33% of 325 yes doublearrays.scala:240
vecxt.doublearrays$.cbrt @HotPath 108 33% of 325 yes doublearrays.scala:229
vecxt.doublearrays$.atan @HotPath 108 33% of 325 yes doublearrays.scala:218
vecxt.doublearrays$.asin @HotPath 108 33% of 325 yes doublearrays.scala:207
vecxt.doublearrays$.acos @HotPath 108 33% of 325 yes doublearrays.scala:196
vecxt.doublearrays$.abs @HotPath 108 33% of 325 yes doublearrays.scala:184
vecxt.intarrays$.$less @HotPath 106 33% of 325 yes intarrays.scala:48
vecxt.intarrays$.$less$eq @HotPath 106 33% of 325 yes intarrays.scala:52
vecxt.intarrays$.$greater @HotPath 106 33% of 325 yes intarrays.scala:56
vecxt.intarrays$.$greater$eq @HotPath 106 33% of 325 yes intarrays.scala:60
vecxt.intarrays$.$eq$colon$eq @HotPath 106 33% of 325 yes intarrays.scala:40
vecxt.intarrays$.$bang$colon$eq @HotPath 106 33% of 325 yes intarrays.scala:44
vecxt.floatarrays$.unary_$minus @HotPath 104 32% of 325 yes floatarrays.scala:116
vecxt.floatarrays$.tanh @HotPath 104 32% of 325 yes floatarrays.scala:305
vecxt.floatarrays$.tan @HotPath 104 32% of 325 yes floatarrays.scala:294
vecxt.floatarrays$.sqrt @HotPath 104 32% of 325 yes floatarrays.scala:261
vecxt.floatarrays$.sinh @HotPath 104 32% of 325 yes floatarrays.scala:283
vecxt.floatarrays$.sin @HotPath 104 32% of 325 yes floatarrays.scala:272
vecxt.floatarrays$.log1p @HotPath 104 32% of 325 yes floatarrays.scala:250
vecxt.floatarrays$.log10 @HotPath 104 32% of 325 yes floatarrays.scala:239
vecxt.floatarrays$.log @HotPath 104 32% of 325 yes floatarrays.scala:228
vecxt.floatarrays$.expm1 @HotPath 104 32% of 325 yes floatarrays.scala:217
vecxt.floatarrays$.exp @HotPath 104 32% of 325 yes floatarrays.scala:206
vecxt.floatarrays$.cosh @HotPath 104 32% of 325 yes floatarrays.scala:195
vecxt.floatarrays$.cos @HotPath 104 32% of 325 yes floatarrays.scala:184
vecxt.floatarrays$.cbrt @HotPath 104 32% of 325 yes floatarrays.scala:173
vecxt.floatarrays$.atan @HotPath 104 32% of 325 yes floatarrays.scala:162
vecxt.floatarrays$.asin @HotPath 104 32% of 325 yes floatarrays.scala:151
vecxt.floatarrays$.acos @HotPath 104 32% of 325 yes floatarrays.scala:140
vecxt.floatarrays$.abs @HotPath 104 32% of 325 yes floatarrays.scala:128
vecxt.intarrays$.mean @Thin 11 31% of 35 no intarrays.scala:287
vecxt.doublearrays$.sumSIMD @AllocFree @HotPath 102 31% of 325 yes doublearrays.scala:703
vecxt.doublearrays$.tanh$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.tan$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.sqrt$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.sinh$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.sin$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.log1p$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.log10$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.log$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.expm1$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.exp$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.cosh$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.cos$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.cbrt$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.atan$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.asin$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.acos$bang @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.abs$bang @AllocFree @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.doublearrays$.$minus$bang @AllocFree @HotPath 98 30% of 325 yes doublearrays.scala:151
vecxt.floatarrays$.increments @HotPath 97 30% of 325 yes floatarrays.scala:660
vecxt.doublearrays$.$times$times$bang @HotPath 95 29% of 325 yes doublearrays.scala:372
vecxt.intarrays$.$less @HotPath 94 29% of 325 yes intarrays.scala:139
vecxt.intarrays$.$less$eq @HotPath 94 29% of 325 yes intarrays.scala:143
vecxt.intarrays$.$greater @HotPath 94 29% of 325 yes intarrays.scala:147
vecxt.intarrays$.$greater$eq @HotPath 94 29% of 325 yes intarrays.scala:151
vecxt.intarrays$.$eq$colon$eq @HotPath 94 29% of 325 yes intarrays.scala:131
vecxt.intarrays$.$bang$colon$eq @HotPath 94 29% of 325 yes intarrays.scala:135
vecxt.floatarrays$.tanh$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.tan$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.sqrt$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.sinh$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.sin$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.log1p$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.log10$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.log$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.expm1$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.exp$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.cosh$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.cos$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.cbrt$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.atan$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.asin$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.acos$bang @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.abs$bang @AllocFree @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.floatarrays$.$minus$bang @AllocFree @HotPath 93 29% of 325 yes floatarrays.scala:96
vecxt.intarrays$.variance @Thin 10 29% of 35 no intarrays.scala:304
vecxt.intarrays$.std @Thin 10 29% of 35 no intarrays.scala:361
vecxt.doublearrays$.variance @AllocFree @Thin 10 29% of 35 no doublearrays.scala:541
vecxt.doublearrays$.$plus$eq @AllocFree @HotPath 90 28% of 325 yes doublearrays.scala:1087
vecxt.doublearrays$.$minus$eq @AllocFree @HotPath 90 28% of 325 yes doublearrays.scala:1175
vecxt.doublearrays$.$times$eq @AllocFree @HotPath 88 27% of 325 yes doublearrays.scala:1250
vecxt.doublearrays$.productSIMD @AllocFree @HotPath 86 26% of 325 yes doublearrays.scala:726
vecxt.floatarrays$.$times$times$bang @HotPath 85 26% of 325 yes floatarrays.scala:316
vecxt.doublearrays$.sumSIMD @AllocFree @HotPath 85 26% of 325 yes doublearrays.scala:677
vecxt.doublearrays$.fma$bang @AllocFree @HotPath 85 26% of 325 yes doublearrays.scala:1150
vecxt.intarrays$.$plus$eq @AllocFree @HotPath 84 26% of 325 yes intarrays.scala:555
vecxt.intarrays$.$minus$eq @AllocFree @HotPath 84 26% of 325 yes intarrays.scala:527
vecxt.floatarrays$.$times$eq @AllocFree @HotPath 84 26% of 325 yes floatarrays.scala:893
vecxt.floatarrays$.$plus$eq @AllocFree @HotPath 84 26% of 325 yes floatarrays.scala:791
vecxt.floatarrays$.$minus$eq @AllocFree @HotPath 84 26% of 325 yes floatarrays.scala:831
vecxt.ndarrayOps.expandDims @Thin 9 26% of 35 no ndarrayOps.scala
vecxt.intarrays.stdDev @Thin 9 26% of 35 no intarrays.scala
vecxt.intarrays.meanAndVariance @Thin 9 26% of 35 no intarrays.scala
vecxt.intarrays.lte @Thin 9 26% of 35 no intarrays.scala
vecxt.intarrays.lte @Thin 9 26% of 35 no intarrays.scala
vecxt.intarrays.lt @Thin 9 26% of 35 no intarrays.scala
vecxt.intarrays.lt @Thin 9 26% of 35 no intarrays.scala
vecxt.intarrays.gte @Thin 9 26% of 35 no intarrays.scala
vecxt.intarrays.gte @Thin 9 26% of 35 no intarrays.scala
vecxt.intarrays.gt @Thin 9 26% of 35 no intarrays.scala
vecxt.intarrays.gt @Thin 9 26% of 35 no intarrays.scala
vecxt.intarrays$.variance @Thin 9 26% of 35 no intarrays.scala:291
vecxt.intarrays$.stdDev @Thin 9 26% of 35 no intarrays.scala:364
vecxt.intarrays$.std @Thin 9 26% of 35 no intarrays.scala:357
vecxt.intarrays$.meanAndVariance @Thin 9 26% of 35 no intarrays.scala:308
vecxt.doublearrays.meanAndVariance @Thin 9 26% of 35 no doublearrays.scala
vecxt.doublearrays$.meanAndVariance @Thin 9 26% of 35 no doublearrays.scala:555
vecxt.intarrays$.minSIMD @AllocFree @HotPath 82 25% of 325 yes intarrays.scala:575
vecxt.intarrays$.maxSIMD @AllocFree @HotPath 82 25% of 325 yes intarrays.scala:595
vecxt.floatarrays$.productSIMD @AllocFree @HotPath 82 25% of 325 yes floatarrays.scala:539
vecxt.intarrays$.sumSIMD @AllocFree @HotPath 81 25% of 325 yes intarrays.scala:265
vecxt.floatarrays$.sumSIMD @AllocFree @HotPath 81 25% of 325 yes floatarrays.scala:518
vecxt.floatarrays$.fma$bang @AllocFree @HotPath 80 25% of 325 yes floatarrays.scala:342
vecxt.floatarrays$.$times$eq @AllocFree @HotPath 78 24% of 325 yes floatarrays.scala:945
vecxt.ndarray.shapeArray @Thin 8 23% of 35 no ndarray.scala
vecxt.matrix$Matrix.rows @Thin 8 23% of 35 no matrix.scala:195
vecxt.matrix$Matrix.rowStride @Thin 8 23% of 35 no matrix.scala:201
vecxt.matrix$Matrix.offset @Thin 8 23% of 35 no matrix.scala:207
vecxt.matrix$Matrix.numel @Thin 8 23% of 35 no matrix.scala:210
vecxt.matrix$Matrix.isDenseRowMajor @Thin 8 23% of 35 no matrix.scala:216
vecxt.matrix$Matrix.isDenseColMajor @Thin 8 23% of 35 no matrix.scala:213
vecxt.matrix$Matrix.hasSimpleContiguousMemoryLayout @Thin 8 23% of 35 no matrix.scala:228
vecxt.matrix$Matrix.cols @Thin 8 23% of 35 no matrix.scala:198
vecxt.matrix$Matrix.colStride @Thin 8 23% of 35 no matrix.scala:204
vecxt.intarrays$.countsToIdx @HotPath 70 22% of 325 yes intarrays.scala:244
vecxt.intarrays$.$minus$eq @AllocFree @HotPath 67 21% of 325 yes intarrays.scala:408
vecxt.IntArraysX$.select @HotPath 44 14% of 325 yes intarray.scala:74
vecxt.IntArraysX$.contiguous @HotPath 39 12% of 325 yes intarray.scala:111
vecxt.floatarrays$.cumsum$bang @AllocFree @HotPath 27 8% of 325 yes floatarrays.scala:686
vecxt.doublearrays$.cumsum$bang @AllocFree @HotPath 27 8% of 325 yes doublearrays.scala:976
vecxt.floatarrays$.$plus$eq @AllocFree @HotPath 24 7% of 325 no floatarrays.scala:750
Method sizes
band methods
<= 6 (trivial, always inlined) 1435
7-35 (inlinable cold) 3670
36-325 (inlinable when hot) 674
326-8000 (not inlined) 112
> 8000 (NEVER JIT COMPILED) 0
bytes method module at
4635 CheatsheetTest$.matrixRangeSlicing experiments cheatsheet.scala:118
3801 CheatsheetTest$.ndArrayInt experiments cheatsheet.scala:416
3779 CheatsheetTest$.ndArrayBoolean experiments cheatsheet.scala:430
3143 CheatsheetTest$.matrixReverseSlicing experiments cheatsheet.scala:125
3061 CheatsheetTest$.ndArrayFloat experiments cheatsheet.scala:395
3015 CheatsheetTest$.ndArrayFloatReductions experiments cheatsheet.scala:405
2500 vecxt_re.Tower.show vecxt_re Tower.scala:63
2483 CheatsheetTest$.matrixCreation experiments cheatsheet.scala:72
2120 CheatsheetTest$.matrixOps experiments cheatsheet.scala:167
1568 vecxt.ndarrayOps$.apply vecxt ndarrayOps.scala:452
1225 vecxt.JvmFloatMatrix$.floatmatrixSubVector vecxt floatmatrix.scala:324
1200 CheatsheetTest$.arrayManipulation experiments cheatsheet.scala:305
1189 vecxt.JvmDoubleMatrix$.$plus$eq vecxt doublematrix.scala:220
1165 vecxt.JvmFloatMatrix$.floatmatrixAddVectorInPlace vecxt floatmatrix.scala:242
1165 vecxt.JvmFloatMatrix$.floatmatrixSubVectorInPlace vecxt floatmatrix.scala:342
1043 vecxt.JvmDoubleMatrix$.$plus$eq vecxt doublematrix.scala:302
1028 vecxt.JvmFloatMatrix$.floatmatrixAddScalarInPlace vecxt floatmatrix.scala:418
1028 vecxt.JvmFloatMatrix$.floatmatrixSubScalarInPlace vecxt floatmatrix.scala:490
969 vecxt_re.Scenarr$.combine vecxt_re scenarr.scala:160
908 vecxt.Svd$.svd vecxt svd.scala:135
902 vecxt.Svd$.pinv vecxt svd.scala:42
896 vecxt_re.NegativeBinomial$.volweightedMle vecxt_re NegativeBinomial.scala:281
878 vecxt_re.NegativeBinomial$.mle vecxt_re NegativeBinomial.scala:151
822 vecxt.DoubleMatrix$.hadamard vecxt doublematrix.scala:263
816 vecxt_re.Layer.equals vecxt_re Layer.scala:21
Proposed baseline 
{
  "jdkMajor": 25,
  "c9": { "totalBytes": 36598, "distinctOps": 205 },
  "annotated": {
    "vecxt.IntArraysX$.contiguous([I)Z": 39,
    "vecxt.IntArraysX$.select([I[I)[I": 44,
    "vecxt.NDArrayDoubleOps$.binaryOpGeneral(Lvecxt/ndarray$NDArray;Lvecxt/ndarray$NDArray;Lscala/Function2;)Lvecxt/ndarray$NDArray;": 186,
    "vecxt.NDArrayDoubleOps$.binaryOpInPlaceGeneral(Lvecxt/ndarray$NDArray;Lvecxt/ndarray$NDArray;Lscala/Function2;)V": 153,
    "vecxt.NDArrayDoubleOps$.compareGeneral(Lvecxt/ndarray$NDArray;Lvecxt/ndarray$NDArray;Lscala/Function2;)Lvecxt/ndarray$NDArray;": 186,
    "vecxt.NDArrayDoubleOps$.compareScalarGeneral(Lvecxt/ndarray$NDArray;DLscala/Function2;)Lvecxt/ndarray$NDArray;": 157,
    "vecxt.NDArrayDoubleOps$.unaryOpGeneral(Lvecxt/ndarray$NDArray;Lscala/Function1;)Lvecxt/ndarray$NDArray;": 152,
    "vecxt.NDArrayFloatOps$.binaryOpGeneral(Lvecxt/ndarray$NDArray;Lvecxt/ndarray$NDArray;Lscala/Function2;)Lvecxt/ndarray$NDArray;": 195,
    "vecxt.NDArrayFloatOps$.binaryOpInPlaceGeneral(Lvecxt/ndarray$NDArray;Lvecxt/ndarray$NDArray;Lscala/Function2;)V": 162,
    "vecxt.NDArrayFloatOps$.compareGeneral(Lvecxt/ndarray$NDArray;Lvecxt/ndarray$NDArray;Lscala/Function2;)Lvecxt/ndarray$NDArray;": 195,
    "vecxt.NDArrayFloatOps$.compareScalarGeneral(Lvecxt/ndarray$NDArray;FLscala/Function2;)Lvecxt/ndarray$NDArray;": 165,
    "vecxt.NDArrayIntOps$.binaryOpGeneral(Lvecxt/ndarray$NDArray;Lvecxt/ndarray$NDArray;Lscala/Function2;)Lvecxt/ndarray$NDArray;": 186,
    "vecxt.NDArrayIntOps$.binaryOpInPlaceGeneral(Lvecxt/ndarray$NDArray;Lvecxt/ndarray$NDArray;Lscala/Function2;)V": 153,
    "vecxt.NDArrayIntOps$.compareGeneral(Lvecxt/ndarray$NDArray;Lvecxt/ndarray$NDArray;Lscala/Function2;)Lvecxt/ndarray$NDArray;": 186,
    "vecxt.NDArrayIntOps$.compareScalarGeneral(Lvecxt/ndarray$NDArray;ILscala/Function2;)Lvecxt/ndarray$NDArray;": 156,
    "vecxt.NDArrayIntOps$.unaryOpGeneral(Lvecxt/ndarray$NDArray;Lscala/Function1;)Lvecxt/ndarray$NDArray;": 152,
    "vecxt.doublearrays$.$div([DDII[DI)V": 116,
    "vecxt.doublearrays$.$minus$bang([D)V": 98,
    "vecxt.doublearrays$.$minus$eq([DD)V": 90,
    "vecxt.doublearrays$.$minus$eq([D[D)V": 26,
    "vecxt.doublearrays$.$minus([DDII[DI)V": 116,
    "vecxt.doublearrays$.$minus([D[D)[D": 24,
    "vecxt.doublearrays$.$plus$eq([DD)V": 90,
    "vecxt.doublearrays$.$plus([DDII[DI)V": 116,
    "vecxt.doublearrays$.$times$eq([D[D)V": 88,
    "vecxt.doublearrays$.$times$times$bang([DD)V": 95,
    "vecxt.doublearrays$.abs$bang([D)V": 98,
    "vecxt.doublearrays$.abs([D)[D": 108,
    "vecxt.doublearrays$.acos$bang([D)V": 98,
    "vecxt.doublearrays$.acos([D)[D": 108,
    "vecxt.doublearrays$.asin$bang([D)V": 98,
    "vecxt.doublearrays$.asin([D)[D": 108,
    "vecxt.doublearrays$.atan$bang([D)V": 98,
    "vecxt.doublearrays$.atan([D)[D": 108,
    "vecxt.doublearrays$.cbrt$bang([D)V": 98,
    "vecxt.doublearrays$.cbrt([D)[D": 108,
    "vecxt.doublearrays$.clamp$bang([DDD)V": 180,
    "vecxt.doublearrays$.cos$bang([D)V": 98,
    "vecxt.doublearrays$.cos([D)[D": 108,
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  }
}

@Quafadas
Quafadas merged commit f581b78 into main Aug 11, 2026
7 checks passed
@Quafadas
Quafadas deleted the matrixinstance-general-gather branch August 11, 2026 14:43
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