7 posts
SciRS2 0.5.1 is a correctness and Pure-Rust hardening release: exact autograd gradients for matrix functions (Sylvester / Daleckii–Krein / reduced-SVD VJP), a restored CoreError #[non_exhaustive] contract, honest GPU/CUDA reporting, public Zarr v2/v3, and the removal of the C/MPFR rug, C rusqlite, and tokenizers dependencies. 36,606 tests, zero warnings — the sovereign scientific-computing layer of the COOLJAPAN ecosystem.
High-performance numerical computing library in pure Rust — the production-grade NumPy alternative. 222k+ SLoC, 4,704+ tests, 128+ SIMD-vectorized functions, N-dimensional arrays, advanced linalg via OxiBLAS, automatic differentiation, FFT, GPU (wgpu), Python bindings (PyO3), Arrow interop. 80–172% of OpenBLAS performance, zero C/Fortran deps. The sovereign numerical layer for SciRS2 and the entire COOLJAPAN ecosystem (now 21M+ SLoC total).
Production-grade BLAS and LAPACK entirely in Rust. Up to 172% of OpenBLAS performance on Apple M3, full sparse solvers, f128 precision, RuntimeAutoTuner, no_std support. The sovereign mathematical foundation for SciRS2 and the entire COOLJAPAN scientific computing stack.
OxiBLAS 0.2.0 is a major step up: cache-oblivious recursive and parallel factorizations, batched BLAS, runtime auto-tuning, multifrontal sparse solvers, mixed-precision refinement, NUMA-aware allocation, and no_std support — with the Fortran FFI retired in favor of a fully pure Rust workspace.
OxiBLAS 0.1.2 brings complex-aware LAPACK to ndarray: SVD and QR for complex matrices, Cholesky and eigendecomposition for Hermitian matrices, plus relaxed trait bounds so solve() works with Complex32/Complex64. Pure Rust BLAS/LAPACK, no C or Fortran.
OxiBLAS 0.1.1 fixes a critical sign bug in the symmetric eigenvalue QR algorithm so eigenvectors accumulate correctly for matrices that need multiple QR sweeps. The pure Rust BLAS/LAPACK foundation now powering the just-launched SciRS2 core.
The first public release of OxiBLAS — a pure Rust implementation of BLAS and LAPACK. Full Level 1/2/3 BLAS, LU/Cholesky/QR/SVD/EVD, 9 sparse formats, f16/f128 precision, and DGEMM already matching OpenBLAS on large matrices. No C, no Fortran, no MKL.