OpenSolvers explores how open-source scientific software runs on real hardware — starting with RISC-V boards and the tools that make that practical (EESSI, OpenBLAS, and friends). This site documents what we learn along the way.

What we're working on

We benchmark scientific libraries and applications on consumer RISC-V boards through the EESSI stack — from BLAS kernels up to full app runs — swapping fixed OpenBLAS builds via FlexiBLAS without rebuilding downstream code.

Recent highlights on the Orange Pi RV2 (SpaceMiT X60, RVV): fixing an OpenBLAS gemv_n bug restores correctness across BLAS, LAPACK, ELPA, HPL, and Quantum ESPRESSO — with patched RVV reaching 10.53 GFLOP/s on Linpack, 1.58× on a dense eigensolve, and 1.31× on a 64-atom Si DFT SCF.

Scientific libs

Library-level probes — performance and numerical correctness:

• BLAS — OpenBLAS improvements (U74 kernel, X60 gemv_n fix)
• DGEMM — bench_dgemm + difftest performance and correctness probes
• NumPy — bench_blas.py DGEMM and eigvalsh through the SciPy stack
• LAPACK — LAPACK path via NumPy eigvalsh
• ELPA — dense eigensolver (CP2K / VASP class workloads)

Apps

End-to-end application benchmarks on the same boards and EESSI toolchain:

• HPL — High Performance Linpack; cross-board summary and A/B configs from opensolvers/benchmarks
• Quantum ESPRESSO — plane-wave DFT SCF (pw.x); whole-application BLAS backend A/B with per-routine timers

Boards

• StarFive VisionFive 2 — JH7110 SoC, 4× SiFive U74 (rv64gc). U74 OpenBLAS tuning: HPL 3.13 → 5.28 GFLOP/s.
• Orange Pi RV2 — SpaceMiT K1, 8× X60 (RVV). Fixed OpenBLAS: HPL FAILED (nan) → 10.53 GFLOP/s; ELPA 34.81 s (vs 54.92 s scalar).
• Banana Pi F3 — same K1 / X60 SoC, 3.7 GB RAM. HPL FAILED (nan) → 11.52 GFLOP/s; NumPy DGEMM up to 17.51 GFLOP/s on patched RVV.

Use the menu above to jump to a board, app, or scientific lib page.