docs for ndarray v0.15.0: /docs/0.15/, float32 sum blocks on riscv64 and loong64

README.md

@@ -19,7 +19,7 @@ describes**:
 
 | Source | Published under |
 | --- | --- |
-| branch `main`, with `params.ndarray.version: v0.14.0` in `hugo.yaml` | `https://go-ndarray.github.io/docs/<MAJOR.MINOR>/`, here `/docs/0.14/` |
+| branch `main`, with `params.ndarray.version: v0.15.0` in `hugo.yaml` | `https://go-ndarray.github.io/docs/<MAJOR.MINOR>/`, here `/docs/0.15/` |
 | the newest version | also `https://go-ndarray.github.io/docs/latest/` |
 
 `https://go-ndarray.github.io/docs/` redirects to `latest/`. Each push to

content/en/dtypes.md

@@ -8,7 +8,7 @@ description: "Why the dtype is a run-time property of one Array type, how arrays
 tags: [dtypes, numpy]
 ---
 
-> Mirrored from [`docs/design-dtypes.md`](https://github.com/go-ndarray/ndarray/blob/v0.14.0/docs/design-dtypes.md) at v0.14.0.
+> Mirrored from [`docs/design-dtypes.md`](https://github.com/go-ndarray/ndarray/blob/v0.15.0/docs/design-dtypes.md) at v0.15.0.
 
 > Status: implemented (Phase 2). Supersedes decision 4 of
 > [plan-ndarray.md](roadmap.md) ("`float64` first").

content/en/linalg.md

@@ -8,9 +8,9 @@ description: "Solve, Inv, Det, Cholesky, QR, SVD, Eig and the rest of numpy.lina
 tags: [linalg, numpy]
 ---
 
-> Mirrored from [`docs/linalg.md`](https://github.com/go-ndarray/ndarray/blob/v0.14.0/docs/linalg.md) at v0.14.0.
+> Mirrored from [`docs/linalg.md`](https://github.com/go-ndarray/ndarray/blob/v0.15.0/docs/linalg.md) at v0.15.0.
 
-Package [`linalg`](https://pkg.go.dev/github.com/go-ndarray/ndarray@v0.14.0/linalg) gives ndarray arrays the functions of
+Package [`linalg`](https://pkg.go.dev/github.com/go-ndarray/ndarray@v0.15.0/linalg) gives ndarray arrays the functions of
 `numpy.linalg`, written in Go without cgo or assembly of its own: the
 matrix products it relies on are the ndarray package's packed SIMD GEMM.
 

content/en/performance.md

@@ -930,10 +930,28 @@ and multiplication wrap modulo 2⁶⁴, so the order changes nothing). They also
 consume the slice instead of indexing `i+1…i+3`, which had left four bounds
 checks per iteration in the amd64 code; now there are none.
 
-Left: ppc64le, s390x, loong64 and riscv64 run the scalar block loop. ppc64le
-needs `xvaddsp`, which Go's assembler lacks: go-asmgen/asmgen#27 adds the
-VSX single-precision family to its registry, with references from GNU as
-and llvm-mc.
+**riscv64 and loong64 (2026-10-18).** RVV: `VSETIVLI $8, E32, M2` gives
+vl = 8 on every VLEN ≥ 128, and `VFADDVV` adds a block to the two-register
+group. LSX: the eight accumulators sit in two 128-bit registers, and
+`VFADD.S` (`VADDF`) adds a block. Each runs behind the feature bit its float64
+kernels use. Both were run on the real hardware: the kernel tests passed
+there, and a sabotaged kernel failed them, so the kernel path is the one
+taken. Same A/B protocol, idle hosts:
+
+| float32 `Sum`, 2¹⁴ / 2²⁰ | change | controls |
+|---|---:|---|
+| SpacemiT X60 (cfarm95, RVV, VLEN 256) | −30% / −41% | float64, int32 ~ |
+| Loongson 3C5000L (cfarm401, LSX) | −24% / −24% | float64, int32 ~ |
+
+The first loong64 kernel used one 256-bit LASX register for all eight
+accumulators, and was **26–47% slower** than the scalar loop. Every block's
+addition waited on the previous one, a single dependency chain, where the
+scalar loop has eight. Two registers give two chains, as SSE2 does on amd64.
+
+Left: ppc64le and s390x run the scalar block loop. Go's assembler lacks
+`xvaddsp` (ppc64) and `vfasb` (s390x). go-asmgen/asmgen#27 adds the VSX
+single-precision family to go-asmgen's registry, with references from GNU as
+and llvm-mc, and needs a cmd/asm patch of its own.
 
 ## SIMD coverage
 

content/en/random.md

@@ -8,9 +8,9 @@ description: "numpy.random with NumPy's numbers for the same seed: the bit gener
 tags: [random, numpy]
 ---
 
-> Mirrored from [`docs/random.md`](https://github.com/go-ndarray/ndarray/blob/v0.14.0/docs/random.md) at v0.14.0.
+> Mirrored from [`docs/random.md`](https://github.com/go-ndarray/ndarray/blob/v0.15.0/docs/random.md) at v0.15.0.
 
-Package [`random`](https://pkg.go.dev/github.com/go-ndarray/ndarray@v0.14.0/random) is `numpy.random` over go-ndarray arrays. Its goal
+Package [`random`](https://pkg.go.dev/github.com/go-ndarray/ndarray@v0.15.0/random) is `numpy.random` over go-ndarray arrays. Its goal
 is migration: a notebook moved from NumPy to Go keeps its numbers for the same
 seed.
 
@@ -24,7 +24,7 @@ u, _ := random.Rand(3)                // np.random.rand(3)
 ```
 
 `Example`, `ExampleSeed` and `ExampleGenerator_ChoiceN` in
-[`example_test.go`](https://github.com/go-ndarray/ndarray/blob/v0.14.0/random/example_test.go) print NumPy's own numbers.
+[`example_test.go`](https://github.com/go-ndarray/ndarray/blob/v0.15.0/random/example_test.go) print NumPy's own numbers.
 
 ## What is implemented
 
@@ -118,7 +118,7 @@ random arguments. Used as-is, that made 3% of `np.random.randn` values and
 10% of `lognormal` values one ULP off.
 
 So `random` rounds `log`, `log1p`, `exp`, `expm1` and `pow` **correctly**
-([`libm.go`](https://github.com/go-ndarray/ndarray/blob/v0.14.0/random/libm.go)): a fast evaluation to about 2⁻⁶⁸ with Ziv's
+([`libm.go`](https://github.com/go-ndarray/ndarray/blob/v0.15.0/random/libm.go)): a fast evaluation to about 2⁻⁶⁸ with Ziv's
 rounding test, and a double-double fallback (about 2⁻¹⁰⁰) for the one argument
 in ~2¹⁵ that needs it. Checked against mpmath at 300 bits on 63,000 arguments
 (0 misrounded; the committed `libm.json` keeps 8,400), and the fast paths

content/en/roadmap.md

@@ -8,7 +8,7 @@ description: "The plan for go-ndarray and what ships today."
 tags: [roadmap]
 ---
 
-> Mirrored from [`docs/plan-ndarray.md`](https://github.com/go-ndarray/ndarray/blob/v0.14.0/docs/plan-ndarray.md) at v0.14.0.
+> Mirrored from [`docs/plan-ndarray.md`](https://github.com/go-ndarray/ndarray/blob/v0.15.0/docs/plan-ndarray.md) at v0.15.0.
 
 > Goal: a **pure-Go (CGO=0)** NumPy-style N-dimensional array library, as a
 > **standalone, reusable** module with correct scalar kernels now and a clean
@@ -112,7 +112,7 @@ suite (plus the per-arch CI jobs already wired in `.github/workflows/ci.yml`).
 - **Performance — DONE (beats NumPy on its core ops; matmul at tuned-BLAS
   parity).** Several levers behind the kernel seam, measured honestly vs NumPy
   2.2.4 (see [docs/perf.md](performance.md) and the repo's
-  [BENCHMARKS.md](https://github.com/go-ndarray/ndarray/blob/v0.14.0/BENCHMARKS.md)):
+  [BENCHMARKS.md](https://github.com/go-ndarray/ndarray/blob/v0.15.0/BENCHMARKS.md)):
   - **Multicore.** `Add/Sub/Mul/Div/Map`, `Sum/Max/Min`, the axis reductions,
     `Concatenate`/`Stack`, and the GEMM fan out across `GOMAXPROCS` above a size
     threshold (small arrays stay serial). Plus an allocation fix: the

content/en/security.md

@@ -8,7 +8,7 @@ description: "How to report a vulnerability, what the library guarantees, and th
 tags: [security]
 ---
 
-> Mirrored from [`SECURITY.md`](https://github.com/go-ndarray/ndarray/blob/main/SECURITY.md) at v0.14.0.
+> Mirrored from [`SECURITY.md`](https://github.com/go-ndarray/ndarray/blob/main/SECURITY.md) at v0.15.0.
 
 ## Reporting
 

hugo.yaml

@@ -6,7 +6,7 @@
 # have used since the site was built with MkDocs and mike. The CI sets it
 # through --baseURL, from params.ndarray.version below. This default is the
 # same build.
-baseURL: "https://go-ndarray.github.io/docs/0.14/"
+baseURL: "https://go-ndarray.github.io/docs/0.15/"
 title: "go-ndarray"
 # themes/hextra is the tannevaled/hextra fork, pinned at a release tag:
 # upstream Hextra plus opt-in features (partial menu items, combos,
@@ -97,7 +97,7 @@ params:
   # The ndarray release these pages describe. Its minor version is the
   # documentation's version label (v0.9.1 -> 0.9): the CI reads it here.
   ndarray:
-    version: "v0.14.0"
+    version: "v0.15.0"
 
   navbar:
     displayTitle: true