Phase A: close Layout property-test gaps, add view-creation benchmark by Copilot · Pull Request #114 · Quafadas/vecxt

Copilot · 2026-08-03T10:10:28Z

Two "(property)" tests in layout.test.scala were single-example assertions rather than generator-driven checks, leaving linearIndex injectivity, in-bounds behavior, and submatrix offset composition untested across the general case. The benchmark suite also had no coverage of view creation (transpose/submatrix) in isolation — every existing benchmark builds matrices in @Setup, so a Layout allocation never shows up as anything but rounding error.

Generator-driven Layout tests (vecxt/test/src/layout.test.scala)
- Hand-rolled generators over small dimension/stride/offset tuples (no scalacheck added to vecxt, per project constraint)
- linearIndex injectivity: asserts { linearIndex(i, j) } produces exactly numel distinct values for non-broadcast layouts; broadcast (zero-stride) layouts assert the opposite
- linearIndex in-bounds: every computed index falls in [0, dataLength)
- Submatrix-of-submatrix offset composition: mirrors MatrixInstance.submatrix's arithmetic (newOffset = offset + rowStart*rowStride + colStart*colStride) and checks it composes correctly across two levels of sub-viewing
- transpose round-trip generalized from the single hard-coded case to the generated layout set
New JMH benchmark (benchmark/src/layoutView.scala)
- LayoutViewBenchmark measures transpose and submatrix view creation directly, parameterized over matrix size (10/100/1000)

No production code was changed — this is test/benchmark infrastructure only.

Fixes Layout Follow Ups #111

github-actions · 2026-08-03T10:19:13Z

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()): 8

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	51103	46714	+9.4%
distinct library ops	178	177	+0.6%
bytes per op	287.1	263.9	+8.8%

severity	check	at	method	detail
WARN	C1	`cheatsheet.scala:118`	`CheatsheetTest$.matrixRangeSlicing`	6996 bytes, past 68% of HugeMethodLimit=8000 (assumed)

Annotated methods (47)

method	annotations	bytes	budget used	loop	at
`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:817`
`vecxt.floatarrays$.clamp$bang`	`@AllocFree` `@HotPath`	172	53% of 325	yes	`floatarrays.scala:379`
`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.doublearrays$.fillLinspace`	`@AllocFree` `@HotPath`	133	41% of 325	yes	`doublearrays.scala:34`
`vecxt.intarrays$.dot`	`@AllocFree` `@HotPath`	129	40% of 325	yes	`intarrays.scala:319`
`vecxt.intarrays$.increments`	`@HotPath`	121	37% of 325	yes	`intarrays.scala:188`
`vecxt.ndarray$.mkNDArray`	`@Thin`	13	37% of 35	no	`ndarray.scala:178`
`vecxt.doublearrays$.increments`	`@HotPath`	110	34% of 325	yes	`doublearrays.scala:359`
`vecxt.floatarrays$.increments`	`@HotPath`	97	30% of 325	yes	`floatarrays.scala:614`
`vecxt.doublearrays$.$times$times$bang`	`@HotPath`	95	29% of 325	yes	`doublearrays.scala:334`
`vecxt.doublearrays$.$minus$eq`	`@AllocFree` `@HotPath`	92	28% of 325	yes	`doublearrays.scala:1008`
`vecxt.doublearrays$.$plus$eq`	`@AllocFree` `@HotPath`	90	28% of 325	yes	`doublearrays.scala:943`
`vecxt.doublearrays$.$times$eq`	`@AllocFree` `@HotPath`	88	27% of 325	yes	`doublearrays.scala:1080`
`vecxt.doublearrays$.productSIMD`	`@AllocFree` `@HotPath`	86	26% of 325	yes	`doublearrays.scala:645`
`vecxt.floatarrays$.$times$times$bang`	`@HotPath`	85	26% of 325	yes	`floatarrays.scala:276`
`vecxt.doublearrays$.sumSIMD`	`@AllocFree` `@HotPath`	85	26% of 325	yes	`doublearrays.scala:622`
`vecxt.doublearrays$.fma$bang`	`@AllocFree` `@HotPath`	85	26% of 325	yes	`doublearrays.scala:983`
`vecxt.intarrays$.$plus$eq`	`@AllocFree` `@HotPath`	84	26% of 325	yes	`intarrays.scala:496`
`vecxt.intarrays$.$minus$eq`	`@AllocFree` `@HotPath`	84	26% of 325	yes	`intarrays.scala:469`
`vecxt.floatarrays$.$times$eq`	`@AllocFree` `@HotPath`	84	26% of 325	yes	`floatarrays.scala:836`
`vecxt.floatarrays$.$plus$eq`	`@AllocFree` `@HotPath`	84	26% of 325	yes	`floatarrays.scala:734`
`vecxt.floatarrays$.$minus$eq`	`@AllocFree` `@HotPath`	84	26% of 325	yes	`floatarrays.scala:774`
`vecxt.doublearrays.meanAndVariance`	`@Thin`	9	26% of 35	no	`doublearrays.scala`
`vecxt.doublearrays$.meanAndVariance`	`@Thin`	9	26% of 35	no	`doublearrays.scala:500`
`vecxt.intarrays$.minSIMD`	`@AllocFree` `@HotPath`	82	25% of 325	yes	`intarrays.scala:516`
`vecxt.intarrays$.maxSIMD`	`@AllocFree` `@HotPath`	82	25% of 325	yes	`intarrays.scala:536`
`vecxt.floatarrays$.productSIMD`	`@AllocFree` `@HotPath`	82	25% of 325	yes	`floatarrays.scala:499`
`vecxt.intarrays$.sumSIMD`	`@AllocFree` `@HotPath`	81	25% of 325	yes	`intarrays.scala:233`
`vecxt.floatarrays$.sumSIMD`	`@AllocFree` `@HotPath`	81	25% of 325	yes	`floatarrays.scala:478`
`vecxt.floatarrays$.fma$bang`	`@AllocFree` `@HotPath`	80	25% of 325	yes	`floatarrays.scala:302`
`vecxt.floatarrays$.$times$eq`	`@AllocFree` `@HotPath`	78	24% of 325	yes	`floatarrays.scala:888`
`vecxt.ndarray.shapeArray`	`@Thin`	8	23% of 35	no	`ndarray.scala`
`vecxt.intarrays$.$minus$eq`	`@AllocFree` `@HotPath`	67	21% of 325	yes	`intarrays.scala:351`
`vecxt.floatarrays$.$plus$eq`	`@AllocFree` `@HotPath`	24	7% of 325	no	`floatarrays.scala:693`

Method sizes

band	methods
<= 6 (trivial, always inlined)	1409
7-35 (inlinable cold)	2944
36-325 (inlinable when hot)	597
326-8000 (not inlined)	110
> 8000 (NEVER JIT COMPILED)	0

bytes	method	module	at
6996	`CheatsheetTest$.matrixRangeSlicing`	experiments	`cheatsheet.scala:118`
4480	`CheatsheetTest$.matrixReverseSlicing`	experiments	`cheatsheet.scala:125`
4329	`CheatsheetTest$.ndArrayBoolean`	experiments	`cheatsheet.scala:430`
4126	`CheatsheetTest$.ndArrayInt`	experiments	`cheatsheet.scala:416`
3549	`CheatsheetTest$.ndArrayFloat`	experiments	`cheatsheet.scala:395`
3283	`CheatsheetTest$.matrixOps`	experiments	`cheatsheet.scala:167`
3256	`CheatsheetTest$.matrixCreation`	experiments	`cheatsheet.scala:72`
3167	`CheatsheetTest$.ndArrayFloatReductions`	experiments	`cheatsheet.scala:405`
2500	`vecxt_re.Tower.show`	vecxt_re	`Tower.scala:63`
2042	`vecxt.JvmDoubleMatrix$.$plus$eq`	vecxt	`doublematrix.scala:315`
2028	`vecxt.JvmFloatMatrix$.floatmatrixAddScalarInPlace`	vecxt	`floatmatrix.scala:442`
2028	`vecxt.JvmFloatMatrix$.floatmatrixSubScalarInPlace`	vecxt	`floatmatrix.scala:514`
2023	`CheatsheetTest$.arrayMath`	experiments	`cheatsheet.scala:253`
1921	`CheatsheetTest$.indexingAndSlicing`	experiments	`cheatsheet.scala:98`
1594	`vecxt.ndarrayOps$.apply`	vecxt	`ndarrayOps.scala:434`
1504	`vecxt.JvmFloatMatrix$.floatmatrixSubVector`	vecxt	`floatmatrix.scala:336`
1478	`CheatsheetTest$.arrayManipulation`	experiments	`cheatsheet.scala:305`
1403	`vecxt.Svd$.pinv`	vecxt	`svd.scala:42`
1346	`vecxt.JvmDoubleMatrix$.$plus$eq`	vecxt	`doublematrix.scala:227`
1322	`vecxt.JvmFloatMatrix$.floatmatrixAddVectorInPlace`	vecxt	`floatmatrix.scala:248`
1322	`vecxt.JvmFloatMatrix$.floatmatrixSubVectorInPlace`	vecxt	`floatmatrix.scala:360`
1198	`CheatsheetTest$.matrixFloat`	experiments	`cheatsheet.scala:445`
1102	`vecxt.DoubleMatrix$.hadamard`	vecxt	`doublematrix.scala:169`
1093	`CheatsheetTest$.matrixInt`	experiments	`cheatsheet.scala:461`
990	`CheatsheetTest$.logicalOps`	experiments	`cheatsheet.scala:280`

Proposed baseline

{
  "jdkMajor": 25,
  "c9": { "totalBytes": 51103, "distinctOps": 178 },
  "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$eq([DD)V": 92,
    "vecxt.doublearrays$.$plus$eq([DD)V": 90,
    "vecxt.doublearrays$.$times$eq([D[D)V": 88,
    "vecxt.doublearrays$.$times$times$bang([DD)V": 95,
    "vecxt.doublearrays$.clamp$bang([DDD)V": 180,
    "vecxt.doublearrays$.fillLinspace([DDD)V": 133,
    "vecxt.doublearrays$.fma$bang([DDD)V": 85,
    "vecxt.doublearrays$.increments([D)[D": 110,
    "vecxt.doublearrays$.meanAndVariance([D)Lscala/Tuple2;": 9,
    "vecxt.doublearrays$.productSIMD([D)D": 86,
    "vecxt.doublearrays$.sumSIMD([D)D": 85,
    "vecxt.doublearrays.meanAndVariance([DLvecxt/VarianceMode;)Lscala/Tuple2;": 9,
    "vecxt.floatarrays$.$minus$eq([FF)V": 84,
    "vecxt.floatarrays$.$plus$eq([FF)V": 84,
    "vecxt.floatarrays$.$plus$eq([F[F)V": 24,
    "vecxt.floatarrays$.$times$eq([FF)V": 78,
    "vecxt.floatarrays$.$times$eq([F[F)V": 84,
    "vecxt.floatarrays$.$times$times$bang([FF)V": 85,
    "vecxt.floatarrays$.clamp$bang([FFF)V": 172,
    "vecxt.floatarrays$.fma$bang([FFF)V": 80,
    "vecxt.floatarrays$.increments([F)[F": 97,
    "vecxt.floatarrays$.productSIMD([F)F": 82,
    "vecxt.floatarrays$.sumSIMD([F)F": 81,
    "vecxt.intarrays$.$minus$eq([II)V": 67,
    "vecxt.intarrays$.$minus$eq([I[I)V": 84,
    "vecxt.intarrays$.$plus$eq([I[I)V": 84,
    "vecxt.intarrays$.dot([I[I)I": 129,
    "vecxt.intarrays$.increments([I)[I": 121,
    "vecxt.intarrays$.maxSIMD([I)I": 82,
    "vecxt.intarrays$.minSIMD([I)I": 82,
    "vecxt.intarrays$.sumSIMD([I)I": 81,
    "vecxt.ndarray$.mkNDArray(Ljava/lang/Object;[I[II)Lvecxt/ndarray$NDArray;": 13,
    "vecxt.ndarray.shapeArray(Lvecxt/ndarray$NDArray;)[I": 8
  }
}

Co-authored-by: Quafadas <24899792+Quafadas@users.noreply.github.com>

Initial plan

c669160

Copilot AI assigned Copilot and Quafadas Aug 3, 2026

Copilot started work on behalf of Quafadas August 3, 2026 10:11 View session

Copilot AI linked an issue Aug 3, 2026 that may be closed by this pull request

Layout Follow Ups #111

Open

Quafadas marked this pull request as ready for review August 3, 2026 10:16

Phase A: close Layout test gaps and add view-creation benchmark

67bafe3

Co-authored-by: Quafadas <24899792+Quafadas@users.noreply.github.com>

Copilot AI changed the title ~~[WIP] Implement follow-up work for layout extraction~~ Phase A: close Layout property-test gaps, add view-creation benchmark Aug 3, 2026

Copilot finished work on behalf of Quafadas August 3, 2026 10:25

Copilot AI requested a review from Quafadas August 3, 2026 10:25

Quafadas merged commit 699435a into main Aug 3, 2026
7 checks passed

Navigation Menu

Uh oh!

Phase A: close Layout property-test gaps, add view-creation benchmark - #114

Phase A: close Layout property-test gaps, add view-creation benchmark#114
Quafadas merged 2 commits into
mainfrom
copilot/layout-follow-ups

Copilot AI commented Aug 3, 2026 •

edited

Loading

Uh oh!

github-actions Bot commented Aug 3, 2026 •

edited

Loading

Uh oh!

Uh oh!

Reviewers

Assignees

Labels

Projects

Milestone

Development

Uh oh!

2 participants

Uh oh!

Conversation

Copilot AI commented Aug 3, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Uh oh!

github-actions Bot commented Aug 3, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Bytecode audit (Tier 1) — ⚠️ no failures, 1 WARN

Uh oh!

Uh oh!

Reviewers

Assignees

Labels

Projects

Milestone

Development

Uh oh!

2 participants

Copilot AI commented Aug 3, 2026 •

edited

Loading

github-actions Bot commented Aug 3, 2026 •

edited

Loading