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tannevaled merged 1 commit into from
Oct 9, 2026
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landing: refresh the performance claims #29

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4 changes: 2 additions & 2 deletions layouts/index.html
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Expand Up @@ -24,7 +24,7 @@ <h1 class="head">NumPy-style n-dimensional arrays in pure Go.<br><span class="ac
<a class="btn btn-primary" href="https://go-ndarray.github.io/docs/">{{ partial "icons/book.html" . }}Read the documentation →</a>
<a class="btn btn-ghost" href="https://github.com/go-ndarray/ndarray">{{ partial "icons/github.html" . }}Browse the source</a>
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<div class="trust"><span><b>{{ len .Site.Params.families }}</b> API families, NumPy's names</span><span><b>5</b> SIMD targets</span><span><b>CGO=0</b> no BLAS shim</span></div>
<div class="trust"><span><b>{{ len .Site.Params.families }}</b> API families, NumPy's names</span><span><b>6</b> SIMD targets</span><span><b>CGO=0</b> no BLAS shim</span></div>
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{{/* Lines taken verbatim from the Example section of go-ndarray/ndarray's README at v0.2.2. */}}
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Expand Down Expand Up @@ -110,7 +110,7 @@ <h2>{{ len .Site.Params.families }} families, <i>NumPy's names</i>.</h2>
<div><div class="n">3.7–5.9×</div><div class="l">single-threaded OpenBLAS 0.3.34 on <code>MatMul</code> from 512² (Zen 3)</div></div>
<div><div class="n">≤ 0.51</div><div class="l">ULP for <code>Exp</code>/<code>Log</code>, ports of Arm's optimized-routines</div></div>
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<p class="lead" style="max-width:none;margin-top:28px"><b>Measured, wins and losses.</b> Against NumPy 2.5.3 + OpenBLAS 0.3.34 on a 16-core AMD Zen 3: <b>faster</b> on whole-array <code>Sum</code>/<code>Mean</code>/<code>Max</code> at 4&nbsp;Mi (2.3–4.5×), <code>SumAxis(1)</code> (2.2×), <code>MaxAxis(1)</code> (1.7×), <code>Exp</code> from 256&nbsp;Ki elements (2.4–5.6×) and <code>MatMul</code> against single-threaded OpenBLAS (3.7–5.9× from 512²); <b>slower</b> against 16-thread OpenBLAS (<code>MatMul</code> 0.55× at 1024², <code>Dot</code>/mat·vec 0.16×) and on allocating elementwise forms outside a <code>Workspace</code>. On Apple silicon the packed GEMM reaches <b>tuned-BLAS parity at 1024²</b> and beats the pure-Go <code>gonum</code> <b>4–10×</b>.</p>
<p class="lead" style="max-width:none;margin-top:28px"><b>Measured, wins and losses.</b> Against NumPy 2.5.3 + OpenBLAS 0.3.34 on a 16-core AMD Zen 3: <b>faster</b> on whole-array <code>Sum</code>/<code>Mean</code>/<code>Max</code> at 4&nbsp;Mi (2.3–4.5×), <code>SumAxis(1)</code> (2.2×), <code>MaxAxis(1)</code> (1.7×), <code>Exp</code> from 256&nbsp;Ki elements (2.4–5.6×) and <code>MatMul</code> against single-threaded OpenBLAS (3.7–5.9× from 512²); <b>slower</b> against 16-thread OpenBLAS (<code>MatMul</code> 0.42× at 512² and 0.51× at 1024², though 1.44× faster at 256²; <code>Dot</code> 0.69× and mat·vec 0.52×, all measured in one run with NumPy) and on allocating elementwise forms outside a <code>Workspace</code>. On Apple silicon the packed GEMM reaches <b>tuned-BLAS parity at 1024²</b> and beats the pure-Go <code>gonum</code> <b>4–10×</b>. On the other targets, against its own scalar code on one core: on a RISC-V SpacemiT X60 the RVV kernels make <code>Sum</code> up to <b>5.8×</b> faster and cut <code>MatMul</code> time by 31–45%; on an IBM z17 the s390x kernels take <code>MatMul</code> 1024² from 340 to <b>70&nbsp;ms</b> and cut <code>Max</code> by 92–96%.</p>
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