NumPy

A SciPy-stack probe for BLAS and LAPACK through NumPy — from opensolvers/benchmarks/numpy (bench_blas.py).

Kernel	NumPy call	Backend	Metric
DGEMM	A @ B	dgemm	GFLOP/s
EIGH	np.linalg.eigvalsh(S)	dsyevd	seconds

Unlike raw OpenBLAS verification, eigvalsh exercises BLAS-2 tridiagonalization — a correctness gate for the same gemv_n bug that breaks HPL and ELPA.

module load SciPy-bundle/2025.07-gfbf-2025b
OMP_NUM_THREADS=8 OPENBLAS_NUM_THREADS=8 python3 bench_blas.py [dgemm_N] [eigh_N]
# defaults: dgemm_N=4096  eigh_N=2048

Swap backends per run: OPENBLAS_CORETYPE=RISCV64_GENERIC or FLEXIBLAS=/path/to/libopenblas.so.

Orange Pi RV2 (8 threads, patched RVV)

Kernel	Scalar	Patched RVV	Speedup
DGEMM N=4096	4.77 GFLOP/s	11.52 GFLOP/s	2.4×
EIGH N=2048	10.54 s	6.72 s	1.6×

Both patched results finite. See LAPACK for the LAPACK angle.

Banana Pi F3 (cross-board, 8 threads)

Kernel	Scalar	Patched RVV	Speedup
DGEMM N=4096	4.91 GFLOP/s	17.51 GFLOP/s	3.6×
EIGH N=2048	9.59 s	5.94 s	1.6×

Stock unpatched RVV: eigvalsh raises LinAlgError: Eigenvalues did not converge; DGEMM alone stays finite at 10.94 GFLOP/s (only gemv is broken — see difftest).

Eigensolver speedup (~1.6×) is lower than DGEMM (~2.4–3.6×) because eigvalsh mixes BLAS-3 with latency-bound BLAS-2 — same pattern as ELPA.