Select GPU devices#

You will learn: how to pick which GPU(s) ACEMD uses on a multi-GPU host.

Prerequisites:

ACEMD installed (see Installation).
A working input.yaml in the current directory.

List your GPUs#

nvidia-smi

The leftmost column of each row in the bottom table is the device index ACEMD expects.

Pick a single GPU#

acemd --device 0

By default ACEMD runs on device 0 — pass --device to override.

Run on multiple GPUs#

Two ways to use multiple GPUs:

acemd --device 0 2          # space-separated list of GPU indices
acemd --ngpus 2             # auto-pick GPUs 0..N-1

If both flags are passed, --ngpus wins — it rewrites --device to [0, ..., ngpus-1]. Use --device if you need specific cards.

When multi-GPU helps — and when it doesn’t#

Running a single simulation across multiple GPUs does not generally improve speed. ACEMD partitions the system across devices, but inter-GPU communication overhead typically cancels — or exceeds — the gain from the extra compute.

Use multiple GPUs for a single simulation when the system does not fit in a single GPU’s VRAM. Splitting across devices is what makes those runs possible at all.

For throughput, run independent simulations on separate GPUs instead: e.g. acemd --device 0 and acemd --device 1 in parallel (or one job per GPU under your scheduler).

From Python#

from acemd import acemd

acemd(".", device=[0, 2])

device is the only GPU-selection keyword accepted by acemd() — ngpus is a CLI-only flag and won’t be recognised here.

Run ACEMD with different platforms#

ACEMD supports three OpenMM platforms via --platform:

CUDA (default) — NVIDIA GPUs.
OpenCL — non-NVIDIA GPUs (AMD, Intel, etc.) at reduced performance.
CPU — testing and debugging without GPU hardware. Orders of magnitude slower than GPU; not for production runs.

acemd --platform CUDA --device 0
acemd --platform OpenCL --device 0
acemd --platform CPU

Gotchas#

CUDA_VISIBLE_DEVICES renumbers the indices ACEMD sees. When the env var is set, the CUDA runtime exposes only the listed physical GPUs and presents them as 0..N-1. Inside that view, --device indices are relative to the visible set, not the nvidia-smi numbering. For example, CUDA_VISIBLE_DEVICES=2,3 acemd --device 0 runs on physical GPU 2, not GPU 0. If you don’t set the env var, --device indices match nvidia-smi directly.
A restart checkpoint (restart.chk) is tied to the GPU model that wrote it. You can’t resume a run started on an RTX 4090 on a different GPU.