Zeros/Arange/Linspace/…), strided views, broadcasting elementwise + ufuncs, reductions (Sum/Max/ArgMax/CumSum/Clip/Where), manipulation (Reshape/Concatenate/Stack), linalg (MatMul/Dot) via a panel-packed cache-blocked GEMM with a go-asmgen SIMD-FMA micro-kernel (NEON 4×8 on arm64, AVX2/FMA 6×8 on amd64, VSX 8×8 on ppc64le, LASX 8×8 on loong64, RVV 6×8 on riscv64), and a Workspace arena for loops (Use/Reset/Detach)."
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@@ -85,7 +85,7 @@ Gated in CI, differentially checked against numpy.
Row-major strided arrays, NumPy broadcasting, and NumPy basic-indexing Slice returning strided views that share data.
Workspace arena for loopsBind the inputs with ws.Use, compute as usual, ws.Reset() at the end of each pass: results stop costing the garbage collector anything. On a Zen 3, x + y on 1 024 elements drops from 2.6 to 0.38 µs (2.1× NumPy).
go-asmgen SIMD kernels on amd64 (SSE2, AVX2/FMA picked at run time), arm64 (NEON), ppc64le (VSX) and loong64 (LASX, when the CPU has it) and riscv64 (RVV, when the CPU has it); s390x and the 32-bit targets run the same pure-Go code those kernels are tested against.
go-asmgen SIMD kernels on amd64 (SSE2, AVX2/FMA picked at run time), arm64 (NEON), ppc64le (VSX), loong64 (LASX, when the CPU has it), riscv64 (RVV, when the CPU has it) and s390x (vector facility, validated under emulation only); the 32-bit targets run the same pure-Go code those kernels are tested against.
Exp/LogPorts of Arm's optimized-routines: worst 0.504 / 0.508 ULP against a 300-bit reference, and correct where Go's amd64 math.Exp returns +Inf (golang/go#81995) and math.Log is wrong on subnormals (golang/go#56600).
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 GOOS/GOARCH pair Go supports; v0.2.0 was also run on real amd64, arm64, ppc64le, riscv64 and loong64 hardware.
Ruby has no cgo-free ndarray (Numo::NArray and NMatrix are C extensions) and gonum's optimized assembly is amd64-only. go-ndarray pairs a portable pure-Go core with multicore fan-out and SIMD kernels.
No cgo, no BLAS shim. A portable pure-Go core, multicore, with SIMD kernels on amd64, arm64, ppc64le, loong64 and riscv64 and a Workspace arena that takes the garbage collector out of loops. Faster than NumPy on whole-array and row reductions, on large Exp and on MatMul against single-threaded BLAS, slower against 16-thread BLAS — the numbers are on the performance page.
No cgo, no BLAS shim. A portable pure-Go core, multicore, with SIMD kernels on amd64, arm64, ppc64le, loong64, riscv64 and s390x and a Workspace arena that takes the garbage collector out of loops. Faster than NumPy on whole-array and row reductions, on large Exp and on MatMul against single-threaded BLAS, slower against 16-thread BLAS — the numbers are on the performance page.