[tannevaled]

This PR contains the following updates:

Package	Change	Age	Confidence
github.com/go-ndarray/ndarray	v0.34.0 → v0.37.5

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Release Notes

go-ndarray/ndarray (github.com/go-ndarray/ndarray)

v0.37.5

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What's Changed

• perf: refbench against NumPy on one core; replace the multicore page by @​tannevaled in #​105

Full Changelog: go-ndarray/ndarray@v0.37.4...v0.37.5

v0.37.4

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What's Changed

• docs: float64 Exp on 16 cores is slowed by allocation, not the kernel by @​tannevaled in #​102

Full Changelog: go-ndarray/ndarray@v0.37.3...v0.37.4

v0.37.3

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What's Changed

• floats v0.11.0: SIMD Max/Min restored on amd64 and arm64 by @​tannevaled in #​101

Full Changelog: go-ndarray/ndarray@v0.37.2...v0.37.3

v0.37.2

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What's Changed

• floats v0.10.0: drop the max/min zero fallback and the moved sum/elementwise assembly by @​tannevaled in #​100

Full Changelog: go-ndarray/ndarray@v0.37.1...v0.37.2

v0.37.1

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SumP runs on several cores again, with the bits of floats.SumPairwise (NumPy order) whatever the core count: it cuts the NumPy pairwise tree where NumPy cuts it and adds the subtrees back up the same tree (#​99). Results are unchanged from v0.37.0.

v0.37.0

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K1 of the go-scikit performance campaign (go-scikit/roadmap#27): ndarray imports go-simd/floats v0.9.0.

Changed result: Sum (and mean, var, std built on it) is now NumPy's pairwise sum (floats.SumPairwise), bit-identical to np.sum of a contiguous 1-D float64 array on every architecture. It replaces lane-parallel sums whose last bits differed from NumPy's and between architectures; it is serial at every size.

Exp, Log and the correctly rounded float32 Exp/Log, and on amd64/arm64 the float64 add/sub/mul/div/sqrt/max/min/clip kernels, are go-simd/floats' (the AVX2 and NEON exp/log kernels written here moved down: #​94, #​95, #​96).

v0.36.0

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What's Changed

• AVX2 kernel for float64 Log: 1.55x faster than NumPy by @​tannevaled in #​96

Full Changelog: go-ndarray/ndarray@v0.35.0...v0.36.0

v0.35.0

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What's Changed

• fix(deps): update dependencies by @​tannevaled in #​89
• docs: design of the dense linear-algebra foundation (scipy.linalg side) by @​tannevaled in #​92
• Float32 results from the Workspace; cheaper range check in the AVX2 log by @​tannevaled in #​94

Full Changelog: go-ndarray/ndarray@v0.34.0...v0.35.0

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