Landing: ppc64le VSX kernels and ndarray v0.2.3

hugo.toml

@@ -12,7 +12,7 @@ disableKinds = ['taxonomy', 'term', 'rss', 'sitemap', '404']
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-  description = "go-ndarray is a pure-Go (CGO=0) NumPy-style n-dimensional array library for float64 — slicing/views, broadcasting, ufuncs, reductions and linear algebra, multicore + SIMD on amd64 and arm64, with a Workspace arena for loops."
+  description = "go-ndarray is a pure-Go (CGO=0) NumPy-style n-dimensional array library for float64 — slicing/views, broadcasting, ufuncs, reductions and linear algebra, multicore + SIMD on amd64, arm64 and ppc64le, with a Workspace arena for loops."
 
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@@ -72,9 +72,9 @@ disableKinds = ['taxonomy', 'term', 'rss', 'sitemap', '404']
 # its card, "kind" as the card's tag, "href" as both links, "blurb" in full.
 [[params.repos]]
   name  = "ndarray"
-  kind  = "library · v0.2.2"
+  kind  = "library · v0.2.3"
   href  = "https://github.com/go-ndarray/ndarray"
-  blurb = "The library. Creation (<code>Zeros</code>/<code>Arange</code>/<code>Linspace</code>/…), strided <strong>views</strong>, broadcasting elementwise + ufuncs, reductions (<code>Sum</code>/<code>Max</code>/<code>ArgMax</code>/<code>CumSum</code>/<code>Clip</code>/<code>Where</code>), manipulation (<code>Reshape</code>/<code>Concatenate</code>/<code>Stack</code>), linalg (<code>MatMul</code>/<code>Dot</code>) via a panel-packed cache-blocked GEMM with a go-asmgen SIMD-FMA micro-kernel (NEON 4&times;8 on arm64, AVX2/FMA 6&times;8 on amd64), and a <code>Workspace</code> arena for loops (<code>Use</code>/<code>Reset</code>/<code>Detach</code>)."
+  blurb = "The library. Creation (<code>Zeros</code>/<code>Arange</code>/<code>Linspace</code>/…), strided <strong>views</strong>, broadcasting elementwise + ufuncs, reductions (<code>Sum</code>/<code>Max</code>/<code>ArgMax</code>/<code>CumSum</code>/<code>Clip</code>/<code>Where</code>), manipulation (<code>Reshape</code>/<code>Concatenate</code>/<code>Stack</code>), linalg (<code>MatMul</code>/<code>Dot</code>) via a panel-packed cache-blocked GEMM with a go-asmgen SIMD-FMA micro-kernel (NEON 4&times;8 on arm64, AVX2/FMA 6&times;8 on amd64, VSX 8&times;8 on ppc64le), and a <code>Workspace</code> arena for loops (<code>Use</code>/<code>Reset</code>/<code>Detach</code>)."
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   name  = "docs"
   kind  = "site"

layouts/index.html

@@ -60,13 +60,13 @@ <h1 class="head">NumPy-style n-dimensional arrays in pure Go.<br><span class="ac
   <section id="features"><div class="wrap">
     <span class="eyebrow">Why go-ndarray</span>
     <h2>The n-dimensional array, <i>without</i> cgo.</h2>
-    <p class="lead">NumPy is the lingua franca of numerical computing, but in Go the choices are <code>gonum</code> (matrix-centric, amd64-only assembly) or a C extension. go-ndarray brings the <b>n-dimensional array</b> vocabulary — shapes, strided views, broadcasting, ufuncs, reductions and linear algebra — to Go with <b>no cgo</b>, cross-compiling to a static binary everywhere. The hot paths are multicore, and SIMD on amd64 and arm64.</p>
+    <p class="lead">NumPy is the lingua franca of numerical computing, but in Go the choices are <code>gonum</code> (matrix-centric, amd64-only assembly) or a C extension. go-ndarray brings the <b>n-dimensional array</b> vocabulary — shapes, strided views, broadcasting, ufuncs, reductions and linear algebra — to Go with <b>no cgo</b>, cross-compiling to a static binary everywhere. The hot paths are multicore, and SIMD on amd64, arm64 and ppc64le.</p>
     <div class="grid" id="quality">
       <div class="card"><div class="ico">🐹</div><h3>Pure Go</h3><p><code>CGO_ENABLED=0</code> across the org. No cgo, no BLAS shim.</p></div>
       <div class="card"><div class="ico">✅</div><h3>100% test coverage</h3><p>Gated in CI, differentially checked against <code>numpy</code>.</p></div>
       <div class="card"><div class="ico">🔢</div><h3>NumPy semantics</h3><p>Row-major strided arrays, <b>NumPy broadcasting</b>, and NumPy basic-indexing <code>Slice</code> returning strided views that share data.</p></div>
       <div class="card"><div class="ico">♻️</div><h3><code>Workspace</code> arena for loops</h3><p>Bind the inputs with <code>ws.Use</code>, compute as usual, <code>ws.Reset()</code> at the end of each pass: results stop costing the garbage collector anything. On a Zen 3, <code>x + y</code> on 1&nbsp;024 elements drops from 2.6 to 0.38&nbsp;µs (2.1× NumPy).</p></div>
-      <div class="card"><div class="ico">⚡</div><h3>SIMD on amd64 &amp; arm64</h3><p>go-asmgen SIMD kernels on amd64 (SSE2, AVX2/FMA picked at run time) and arm64 (NEON); riscv64, loong64, ppc64le, s390x and the 32-bit targets run the same pure-Go code those kernels are tested against.</p></div>
+      <div class="card"><div class="ico">⚡</div><h3>SIMD on amd64, arm64 &amp; ppc64le</h3><p>go-asmgen SIMD kernels on amd64 (SSE2, AVX2/FMA picked at run time), arm64 (NEON) and ppc64le (VSX); riscv64, loong64, s390x and the 32-bit targets run the same pure-Go code those kernels are tested against.</p></div>
       <div class="card"><div class="ico">🎯</div><h3>Accurate <code>Exp</code>/<code>Log</code></h3><p>Ports of Arm's optimized-routines: worst 0.504 / 0.508 ULP against a 300-bit reference, and correct where Go's amd64 <code>math.Exp</code> returns +Inf (<a href="https://github.com/golang/go/issues/81995"><u>golang/go#81995</u></a>) and <code>math.Log</code> is wrong on subnormals (<a href="https://github.com/golang/go/issues/56600"><u>golang/go#56600</u></a>).</p></div>
       <div class="card"><div class="ico">🧪</div><h3>Multi-arch CI</h3><p>The suite runs on amd64, arm64 and 386 natively and on riscv64/loong64/ppc64le/s390x/arm under qemu, on Linux, macOS and Windows, and compiles for every <code>GOOS/GOARCH</code> pair Go supports; v0.2.0 was also run on real amd64, arm64, ppc64le, riscv64 and loong64 hardware.</p></div>
       <div class="card"><div class="ico">💎</div><h3>The cgo-free ndarray that was missing</h3><p>Ruby has no cgo-free ndarray (<code>Numo::NArray</code> and <code>NMatrix</code> are C extensions) and <code>gonum</code>'s optimized assembly is amd64-only. go-ndarray pairs a portable pure-Go core with multicore fan-out and SIMD kernels.</p></div>
@@ -100,7 +100,7 @@ <h2>What is here.</h2>
   <section id="eco" style="padding-top:6px"><div class="wrap">
     <div class="band">
       <h2>The cgo-free ndarray the Go and Ruby ecosystems lacked.</h2>
-      <p>No cgo, no BLAS shim. A portable pure-Go core, multicore, with SIMD kernels on amd64 and arm64 and a <code>Workspace</code> arena that takes the garbage collector out of loops. Faster than NumPy on whole-array and row reductions, on large <code>Exp</code> and on <code>MatMul</code> against single-threaded BLAS, slower against 16-thread BLAS — the numbers are on the <a href="https://go-ndarray.github.io/docs/latest/performance/"><u>performance page</u></a>.</p>
+      <p>No cgo, no BLAS shim. A portable pure-Go core, multicore, with SIMD kernels on amd64, arm64 and ppc64le and a <code>Workspace</code> arena that takes the garbage collector out of loops. Faster than NumPy on whole-array and row reductions, on large <code>Exp</code> and on <code>MatMul</code> against single-threaded BLAS, slower against 16-thread BLAS — the numbers are on the <a href="https://go-ndarray.github.io/docs/latest/performance/"><u>performance page</u></a>.</p>
       <a class="btn btn-ghost" href="https://github.com/go-ndarray/ndarray">Read the source on GitHub →</a>
     </div>
   </div></section>