Document multi-GPU support#19
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- walkthrough/parallelization: expand the "Multiple GPUs" section with the new `dask.multi-gpu` config flag (automatic dask-cuda cluster over all GPUs), the bring-your-own-cluster path, the single-threaded-client requirement, and when multi-GPU actually helps; add a label for cross-referencing. - reference/default_config.yaml: add the `dask.multi-gpu` option (was out of sync with abtem.yaml). - getting_started/install: note the optional `dask-cuda` dependency and link to the walkthrough. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This was referenced Jul 13, 2026
Add a tip to the "Multiple GPUs" section noting that more GPUs provide more aggregate device memory, so memory-bound simulations can scale super-linearly. Quote a large benchmark: ~2.0x on two GPUs and ~4.3x on four (beyond the 4x linear ideal), with the caveat that the exact speed-up depends on the calculation parameters and available hardware. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Documents the multi-GPU feature added in abTEM/abTEM#269.
dask.multi-gpuflag (automatic dask-cuda cluster over all GPUs), thebring-your-own-cluster path, the single-threaded-client requirement, and when
multi-GPU actually helps — including a benchmark showing super-linear scaling
for memory-bound calculations (~2.0× on two GPUs, ~4.3× on four, vs. a
single-GPU baseline). Adds a label for cross-referencing.
dask.multi-gpuoption (theembedded config file was out of sync with
abtem.yaml).dask-cudadependency andlinks to the walkthrough.
🤖 Generated with Claude Code