go-ndarray
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go-ndarray is the numpy equivalent for Go: a float64 n-dimensional array with creation routines, strided views that share data, broadcasting elementwise ops and ufuncs, reductions (incl. arg / cumulative / clip / where), manipulation, and linear algebra (a panel-packed, cache-blocked GEMM) — all in pure Go, with cgo disabled.
The lines in the hero, built into a program against the published module and run. Views share data, broadcasting stretches row over both rows, and MatMul goes through the same cache-blocked SIMD kernel as a 4096² product. The output below is a real run, with nothing edited.
# go-ndarray v0.7.0 — each step of the example, printed +$ go run ./ndcheck +m Array(shape=[2 3], data=[0 1 2 3 4 5]) +m + row Array(shape=[2 3], data=[10 21 32 13 24 35]) +m.T Array(shape=[3 2], data=[0 3 1 4 2 5]) +sum(m,0) Array(shape=[3], data=[3 5 7]) +m[:,0] Array(shape=[2], data=[0 3]) +m @ b Array(shape=[2 2], data=[10 13 28 40])+
NumPy is the lingua franca of numerical computing, but in Go the choices are gonum (matrix-centric, amd64-only assembly) or a C extension. go-ndarray brings the n-dimensional array vocabulary — shapes, strided views, broadcasting, ufuncs, reductions and linear algebra — to Go with no cgo, cross-compiling to a static binary everywhere. The hot paths are multicore, and SIMD on amd64, arm64, ppc64le and loong64.
NumPy is the lingua franca of numerical computing, but in Go the choices are gonum (matrix-centric, amd64-only assembly) or a C extension. go-ndarray brings the n-dimensional array vocabulary — shapes, strided views, broadcasting, ufuncs, reductions and linear algebra — to Go with no cgo, cross-compiling to a static binary everywhere. The hot paths are multicore, and SIMD on amd64, arm64, ppc64le, loong64 and riscv64.
CGO_ENABLED=0 across the org. No cgo, no BLAS shim.
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); riscv64, 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) and loong64 (LASX, when the CPU has it) and riscv64 (RVV, when the CPU has it); riscv64, s390x and 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.
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No cgo, no BLAS shim. A portable pure-Go core, multicore, with SIMD kernels on amd64, arm64, ppc64le and loong64 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 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.