GROMACS

Video: 3.31× GROMACS on RISC-V: Hand-Written RVV Force Backend (Orange Pi RV2) — all videos

GROMACS mdrun PME molecular dynamics on the SpaceMiT X60 — two independent backend axes measured end-to-end:

  1. FFT axis — swap single-precision libfftw3f.so.3 via LD_PRELOAD (stock EESSI build, SIMD: None)
  2. Force axis — hand-written impl_riscv_rvv/ SIMD backend (SIMD: RISCV_RVV) or upstream 2026.3 autovec 1×1 kernel

Benchmark source: opensolvers/benchmarks/gromacs — gen-water-box.sh, run-gmx-fft-ab.sh, and rvv-backend/ (patch + apply-and-build.sh).

FFT microbench numbers: FFTW. BLAS-axis counterpart: Quantum ESPRESSO. Whole-app MD counterpart (RVV-Kokkos, five upstream benches): LAMMPS.

FFT axis — PME 3D-FFT (run-gmx-fft-ab.sh)

Swaps only libfftw3f under one unchanged EESSI GROMACS/2026.2-foss-2025b binary. BLAS pinned to scalar OpenBLAS; serial (-ntmpi 1 -ntomp 1) by design to isolate FFT.

On the stock build GROMACS reports SIMD instructions: None — force kernels are scalar, so PME 3D-FFT / PME mesh are the swapped rows; Force (90% of runtime) is the built-in control.

Correctness — RVV FFT reproduces scalar MD

Same mdrun, same md.tpr, 17.2k-atom SPC water, 2000 steps:

FFT backend Avg potential energy Δ vs scalar
Scalar (libfftw3f, ~944 KB) −236755 kJ/mol —
r5v / RVV (libfftw3f, ~14 MB, 282k RVV instr) −236592 kJ/mol 0.069%

Performance — isolated FFT step (Orange Pi RV2)

Serial, 48×48×48 PME grid. GROMACS cycle accounting (WALL):

Activity Scalar r5v (RVV) Speedup Kind
PME 3D-FFT 21.095 s 17.152 s 1.23× FFT (swapped axis)
PME mesh (total) 73.281 s 69.792 s 1.05× FFT + spread/gather/solve
Force 821.35 s 819.97 s 1.00× scalar control (90%)
Neighbor search 6.731 s 6.714 s 1.00× scalar control

RVV wins ~1.23× on the isolated 3D-FFT step; Force confirms the A/B (821.35 vs 819.97 s, 0.17% noise). Whole-app effect is small because Force dominates.

Force axis — RVV SIMD backend (rvv-backend/)

The scalar Force term that dilutes the FFT swap is itself optimizable. rvv-backend/ ships a hand-written impl_riscv_rvv/ backend for GROMACS 2026.2 (~1545 lines, float/mixed precision) — ARM SVE as the VLA template.

Built on the X60 under EESSI GCC 14.3 with GMX_SIMD=RISCV_RVV. gmx --version reports SIMD instructions: RISCV_RVV; float SIMD unit tests 158/158 pass.

Same serial protocol (SPC water PME, md.tpr, 2000 steps, -ntmpi 1 -ntomp 1 -nb cpu -pme cpu):

Metric Scalar (SIMD: None) RVV (SIMD: RISCV_RVV) Speedup
Force wall 821.35 s 187.37 s 4.38×
Total wall 910.33 s 275.40 s 3.31×
Performance 0.380 ns/day 1.256 ns/day 3.31×
Avg potential energy −236755 kJ/mol −236653 kJ/mol Δ 0.043% (correct)

The profile inverts: with Force cut ~4.4×, PME mesh becomes the leading cost (28% vs 8% of runtime) — so the FFT axis matters more after vectorizing Force.

Status: captured as a patch for reproduction; no upstream PR yet. Double precision not ported.

Force axis — three levers compared

All measured on Orange Pi RV2, same SPC water system, 2000 steps, 1 core (unless noted):

Force lever GROMACS Mechanism Force wall ns/day vs scalar
Scalar 4×4 (baseline) 2026.2 none (SIMD: None) 821.35 s 0.380 1.00×
GCC -march=…v 4×4 autovec 2026.2 compiler on 4×4 kernel ~836 s* 0.372 0.98× (slower)
Upstream 1×1 autovec 2026.3 clang #pragma, GMX_NBNXN_PLAINC_1X1=1 480.14 s 0.543 1.43×
Hand-written impl_riscv_rvv/ 2026.2 GMX_SIMD=RISCV_RVV 187.37 s 1.256 3.31×

*4×4 autovec Force scaled from 1000-step run; ns/day directly comparable.

The 2026.3 autovec 1×1 path (clang ≥ 19, GMX_ENABLE_NBNXM_CPU_VECTORIZATION=on) is a real win — 1.74× whole-app vs the same binary’s default 4×4 — but the hand-written backend is still 2.31× faster whole-app (1.256 vs 0.543 ns/day). Autovec is VLEN-agnostic and upstream-native; the dedicated backend targets fixed VLEN=256 (-mrvv-vector-bits=zvl).

Free lever: threading — serial A/B wastes 7 of 8 cores. Same system at -ntomp 8 reaches ~2.60 ns/day (~6.9× vs serial baseline).

Backend-dilution spectrum

Probe Axis Isolated speedup Whole-app effect
OpenBLAS verification BLAS ~2.3× (pure kernel)
BLIS vs OpenBLAS BLAS ~1.29× (1T, N=4096) (microbench)
QE (DFT SCF) BLAS ~1.5–2.0× on BLAS routines ~1.2–1.3× (FFT untouched)
QE + FFTW FFT ~1.02× on fftw ~0% (FFTW_ESTIMATE)
GROMACS FFT (this page) FFT ~1.23× on PME 3D-FFT small (Force = 90%)
GROMACS Force (rvv-backend/) SIMD ~4.38× on Force ~3.31×

GROMACS FFT is the mirror of QE: QE speeds up BLAS and is diluted by FFT; the FFT swap is diluted by scalar Force. Vectorizing Force moves the whole app — then PME/FFT becomes the next target.

Gotchas

Reproduce

# FFT axis
module load GROMACS/2026.2-foss-2025b
./gen-water-box.sh
bash run-gmx-fft-ab.sh v1

# Force axis (hand-written RVV backend)
cd gromacs/rvv-backend
./apply-and-build.sh /path/to/gromacs-2026.2

Toolchain: GROMACS 2026.2 / foss-2025b (EESSI), FFTW 3.3.10 libfftw3f, Orange Pi RV2 (SpaceMiT X60).