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De-inline the cumsum pair and the BLAS forwarders - #124

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Aug 4, 2026
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De-inline the cumsum pair and the BLAS forwarders#124
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Six methods per file, in doublearrays and its floatarrays mirror. None of them meets any of the three conditions the inlining policy keeps inline for: no closure parameter, no VectorOperators constant to hold, and a concrete element type at every point they touch an element.

cumsum! @hotpath @AllocFree
cumsum @thin
dot @thin
norm @thin

  •      @Thin

-= @thin (the Array overload; see below)

cumsum! is the interesting one. A prefix sum carries a dependency — vec(i) needs the value just written to vec(i - 1) — so it cannot be vectorised and there is no Vector API in the body at all. That makes it the only @AllocFree kernel in the library whose zero is unconditional rather than contingent on C2 applying intrinsics: there is no DoubleVector whose failure to scalarise could put bytes on the heap. It is worth measuring precisely because it is the control case for every other kernel in D1 — if it ever fails, the harness is wrong and not the kernel. Added to D1Suite and D1EAOffSuite for both element types, which brings both suites to 31 tests and keeps every @AllocFree method covered in both.

dot, norm and the array-argument -= are @Thin rather than @HotPath, and the distinction is the annotation's own wording: @HotPath describes "code that runs once per element", and the per-element loop in these is inside netlib's ddot/dnrm2/daxpy, not in the method. What the method does is name the operation and dispatch, which is @Thin's definition. C3's no-backward-branch assertion holds for the same reason and would fail if anyone open-coded the loop back in, which is the right outcome.

None of the three is @AllocFree. blas is JavaBLAS.getInstance, so these call into a third-party pure-Java implementation whose allocation behaviour is not visible from here and has never been measured. Both annotations that turned out to be false — **! and intarrays.dot — were applied by inspection, so inspection is not the standard being used here.

Worth a comment where it lands: -=(Array) is a BLAS forwarder while -=(Double) is a hand-written Vector API loop carrying @hotpath @AllocFree. Same name, different implementations, so different annotations, and not an inconsistency for someone to tidy up later.

Scope note: four of these were named directly; norm and - were added because they sit inside the same block and are the same two shapes, and leaving them inline would half-convert it. add, + and += in the same file are the same shapes again but are left alone — add forwards to +, so it cannot become a clean forwarder until + is emitted, and that ordering is a batch of its own.

Simon Parten @claude
De-inline the cumsum pair and the BLAS forwarders
785cc69 
Six methods per file, in doublearrays and its floatarrays mirror. None of them
meets any of the three conditions the inlining policy keeps `inline` for: no
closure parameter, no VectorOperators constant to hold, and a concrete element
type at every point they touch an element.

  cumsum!    @hotpath @AllocFree
  cumsum     @thin
  dot        @thin
  norm       @thin
  -          @thin
  -=         @thin   (the Array overload; see below)

`cumsum!` is the interesting one. A prefix sum carries a dependency — `vec(i)`
needs the value just written to `vec(i - 1)` — so it cannot be vectorised and
there is no Vector API in the body at all. That makes it the only `@AllocFree`
kernel in the library whose zero is unconditional rather than contingent on C2
applying intrinsics: there is no DoubleVector whose failure to scalarise could put
bytes on the heap. It is worth measuring precisely because it is the control case
for every other kernel in D1 — if it ever fails, the harness is wrong and not the
kernel. Added to D1Suite and D1EAOffSuite for both element types, which brings both
suites to 31 tests and keeps every @AllocFree method covered in both.

`dot`, `norm` and the array-argument `-=` are `@Thin` rather than `@HotPath`, and
the distinction is the annotation's own wording: `@HotPath` describes "code that
runs once per element", and the per-element loop in these is inside netlib's
ddot/dnrm2/daxpy, not in the method. What the method does is name the operation and
dispatch, which is `@Thin`'s definition. C3's no-backward-branch assertion holds for
the same reason and would fail if anyone open-coded the loop back in, which is the
right outcome.

None of the three is `@AllocFree`. `blas` is `JavaBLAS.getInstance`, so these call
into a third-party pure-Java implementation whose allocation behaviour is not
visible from here and has never been measured. Both annotations that turned out to
be false — `**!` and `intarrays.dot` — were applied by inspection, so inspection is
not the standard being used here.

Worth a comment where it lands: `-=(Array)` is a BLAS forwarder while `-=(Double)`
is a hand-written Vector API loop carrying @hotpath @AllocFree. Same name,
different implementations, so different annotations, and not an inconsistency for
someone to tidy up later.

Scope note: four of these were named directly; `norm` and `-` were added because
they sit inside the same block and are the same two shapes, and leaving them
`inline` would half-convert it. `add`, `+` and `+=` in the same file are the same
shapes again but are left alone — `add` forwards to `+`, so it cannot become a
clean forwarder until `+` is emitted, and that ordering is a batch of its own.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Bytecode audit (Tier 1) — ⚠️ no failures, 1 WARN

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 97 — —
total bytes 41872 51103 -18.1%
distinct library ops 206 178 +15.7%
bytes per op 203.3 287.1 -29.2%
severity check at method detail
WARN C1 cheatsheet.scala:118 CheatsheetTest$.matrixRangeSlicing 5643 bytes, past 68% of HugeMethodLimit=8000 (assumed)
Annotated methods (176)
method annotations bytes budget used loop at
vecxt.floatarrays$.$minus$eq @Thin 26 74% of 35 no floatarrays.scala:735
vecxt.doublearrays$.$minus$eq @Thin 26 74% of 35 no doublearrays.scala:999
vecxt.intarrays$.$plus @Thin 24 69% of 35 no intarrays.scala:546
vecxt.intarrays$.$minus @Thin 24 69% of 35 no intarrays.scala:399
vecxt.floatarrays$.$minus @Thin 24 69% of 35 no floatarrays.scala:724
vecxt.doublearrays$.$minus @Thin 24 69% of 35 no doublearrays.scala:987
vecxt.floatarrays$.dot @Thin 23 66% of 35 no floatarrays.scala:650
vecxt.doublearrays$.dot @Thin 23 66% of 35 no doublearrays.scala:978
vecxt.NDArrayFloatOps$.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$bang @AllocFree @HotPath 180 55% of 325 yes doublearrays.scala:872
vecxt.matrix$Layout.linearIndex @Thin 19 54% of 35 no matrix.scala:48
vecxt.floatarrays$.clamp$bang @AllocFree @HotPath 172 53% of 325 yes floatarrays.scala:418
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$.cumsum @Thin 15 43% of 35 no floatarrays.scala:694
vecxt.doublearrays$.cumsum @Thin 15 43% of 35 no doublearrays.scala:959
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:655
vecxt.doublearrays$.norm @Thin 13 37% of 35 no doublearrays.scala:983
vecxt.doublearrays$.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:115
vecxt.floatarrays$.tanh @HotPath 104 32% of 325 yes floatarrays.scala:304
vecxt.floatarrays$.tan @HotPath 104 32% of 325 yes floatarrays.scala:293
vecxt.floatarrays$.sqrt @HotPath 104 32% of 325 yes floatarrays.scala:260
vecxt.floatarrays$.sinh @HotPath 104 32% of 325 yes floatarrays.scala:282
vecxt.floatarrays$.sin @HotPath 104 32% of 325 yes floatarrays.scala:271
vecxt.floatarrays$.log1p @HotPath 104 32% of 325 yes floatarrays.scala:249
vecxt.floatarrays$.log10 @HotPath 104 32% of 325 yes floatarrays.scala:238
vecxt.floatarrays$.log @HotPath 104 32% of 325 yes floatarrays.scala:227
vecxt.floatarrays$.expm1 @HotPath 104 32% of 325 yes floatarrays.scala:216
vecxt.floatarrays$.exp @HotPath 104 32% of 325 yes floatarrays.scala:205
vecxt.floatarrays$.cosh @HotPath 104 32% of 325 yes floatarrays.scala:194
vecxt.floatarrays$.cos @HotPath 104 32% of 325 yes floatarrays.scala:183
vecxt.floatarrays$.cbrt @HotPath 104 32% of 325 yes floatarrays.scala:172
vecxt.floatarrays$.atan @HotPath 104 32% of 325 yes floatarrays.scala:161
vecxt.floatarrays$.asin @HotPath 104 32% of 325 yes floatarrays.scala:150
vecxt.floatarrays$.acos @HotPath 104 32% of 325 yes floatarrays.scala:139
vecxt.floatarrays$.abs @HotPath 104 32% of 325 yes floatarrays.scala:127
vecxt.intarrays$.mean @Thin 11 31% of 35 no intarrays.scala:287
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:659
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:95
vecxt.floatarrays$.tan$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.sqrt$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.sinh$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.sin$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.log1p$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.log10$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.log$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.expm1$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.exp$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.cosh$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.cos$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.cbrt$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.atan$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.asin$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.acos$bang @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.abs$bang @AllocFree @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.floatarrays$.$minus$bang @AllocFree @HotPath 93 29% of 325 yes floatarrays.scala:95
vecxt.intarrays$.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$.$minus$eq @AllocFree @HotPath 92 28% of 325 yes doublearrays.scala:1093
vecxt.doublearrays$.$plus$eq @AllocFree @HotPath 90 28% of 325 yes doublearrays.scala:1028
vecxt.doublearrays$.$times$eq @AllocFree @HotPath 88 27% of 325 yes doublearrays.scala:1165
vecxt.doublearrays$.productSIMD @AllocFree @HotPath 86 26% of 325 yes doublearrays.scala:700
vecxt.floatarrays$.$times$times$bang @HotPath 85 26% of 325 yes floatarrays.scala:315
vecxt.doublearrays$.sumSIMD @AllocFree @HotPath 85 26% of 325 yes doublearrays.scala:677
vecxt.doublearrays$.fma$bang @AllocFree @HotPath 85 26% of 325 yes doublearrays.scala:1068
vecxt.intarrays$.$plus$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:892
vecxt.floatarrays$.$plus$eq @AllocFree @HotPath 84 26% of 325 yes floatarrays.scala:790
vecxt.floatarrays$.$minus$eq @AllocFree @HotPath 84 26% of 325 yes floatarrays.scala:830
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:538
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:517
vecxt.floatarrays$.fma$bang @AllocFree @HotPath 80 25% of 325 yes floatarrays.scala:341
vecxt.floatarrays$.$times$eq @AllocFree @HotPath 78 24% of 325 yes floatarrays.scala:944
vecxt.ndarray.shapeArray @Thin 8 23% of 35 no ndarray.scala
vecxt.matrix$Matrix.rows @Thin 8 23% of 35 no matrix.scala:129
vecxt.matrix$Matrix.rowStride @Thin 8 23% of 35 no matrix.scala:135
vecxt.matrix$Matrix.offset @Thin 8 23% of 35 no matrix.scala:141
vecxt.matrix$Matrix.numel @Thin 8 23% of 35 no matrix.scala:144
vecxt.matrix$Matrix.isDenseRowMajor @Thin 8 23% of 35 no matrix.scala:150
vecxt.matrix$Matrix.isDenseColMajor @Thin 8 23% of 35 no matrix.scala:147
vecxt.matrix$Matrix.hasSimpleContiguousMemoryLayout @Thin 8 23% of 35 no matrix.scala:162
vecxt.matrix$Matrix.cols @Thin 8 23% of 35 no matrix.scala:132
vecxt.matrix$Matrix.colStride @Thin 8 23% of 35 no matrix.scala:138
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.floatarrays$.cumsum$bang @AllocFree @HotPath 27 8% of 325 yes floatarrays.scala:685
vecxt.doublearrays$.cumsum$bang @AllocFree @HotPath 27 8% of 325 yes doublearrays.scala:950
vecxt.floatarrays$.$plus$eq @AllocFree @HotPath 24 7% of 325 no floatarrays.scala:749
Method sizes
band methods
<= 6 (trivial, always inlined) 1411
7-35 (inlinable cold) 3642
36-325 (inlinable when hot) 702
326-8000 (not inlined) 89
> 8000 (NEVER JIT COMPILED) 0
bytes method module at
5643 CheatsheetTest$.matrixRangeSlicing experiments cheatsheet.scala:118
3815 CheatsheetTest$.matrixReverseSlicing experiments cheatsheet.scala:125
3801 CheatsheetTest$.ndArrayInt experiments cheatsheet.scala:416
3779 CheatsheetTest$.ndArrayBoolean experiments cheatsheet.scala:430
3061 CheatsheetTest$.ndArrayFloat experiments cheatsheet.scala:395
3015 CheatsheetTest$.ndArrayFloatReductions experiments cheatsheet.scala:405
2811 CheatsheetTest$.matrixCreation experiments cheatsheet.scala:72
2580 CheatsheetTest$.matrixOps experiments cheatsheet.scala:167
2500 vecxt_re.Tower.show vecxt_re Tower.scala:63
1594 vecxt.ndarrayOps$.apply vecxt ndarrayOps.scala:452
1460 CheatsheetTest$.indexingAndSlicing experiments cheatsheet.scala:98
1435 vecxt.JvmDoubleMatrix$.$plus$eq vecxt doublematrix.scala:315
1420 vecxt.JvmFloatMatrix$.floatmatrixAddScalarInPlace vecxt floatmatrix.scala:442
1420 vecxt.JvmFloatMatrix$.floatmatrixSubScalarInPlace vecxt floatmatrix.scala:514
1308 CheatsheetTest$.arrayManipulation experiments cheatsheet.scala:305
1122 vecxt.Svd$.pinv vecxt svd.scala:42
1028 CheatsheetTest$.matrixFloat experiments cheatsheet.scala:445
984 vecxt.JvmDoubleMatrix$.$plus$eq vecxt doublematrix.scala:227
977 vecxt.JvmFloatMatrix$.floatmatrixSubVector vecxt floatmatrix.scala:336
969 vecxt_re.Scenarr$.combine vecxt_re scenarr.scala:160
960 vecxt.JvmFloatMatrix$.floatmatrixAddVectorInPlace vecxt floatmatrix.scala:248
960 vecxt.JvmFloatMatrix$.floatmatrixSubVectorInPlace vecxt floatmatrix.scala:360
923 CheatsheetTest$.matrixInt experiments cheatsheet.scala:461
908 vecxt.Svd$.svd vecxt svd.scala:135
896 vecxt_re.NegativeBinomial$.volweightedMle vecxt_re NegativeBinomial.scala:281
Proposed baseline 
{
  "jdkMajor": 25,
  "c9": { "totalBytes": 41872, "distinctOps": 206 },
  "annotated": {
    "vecxt.NDArrayDoubleOps$.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$NDArray;": 152,
    "vecxt.doublearrays$.$minus$bang([D)V": 98,
    "vecxt.doublearrays$.$minus$eq([DD)V": 92,
    "vecxt.doublearrays$.$minus$eq([D[D)V": 26,
    "vecxt.doublearrays$.$minus([D[D)[D": 24,
    "vecxt.doublearrays$.$plus$eq([DD)V": 90,
    "vecxt.doublearrays$.$times$eq([D[D)V": 88,
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  }
}

@Quafadas
Quafadas merged commit 3094eca into main Aug 4, 2026
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Copilot AI mentioned this pull request Aug 4, 2026
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