A smaller footprint.
Fewer dispatches.
I contributed compact scratch buffers for CUDA reductions and batched gradient copies in distributed training, with AI assistance and maintainer review.
Mixed-size tensors left most of a rectangular scratch buffer unused. My contribution stores the partial results compactly. Before human review, the first version slowed uniform inputs; I reworked the data-transfer path into one pinned, asynchronous copy.
Mixed-size tensors · RTX 4090 · 96% less scratch memory. This is temporary memory for the operation, not total GPU memory.
In a 200-layer, 400-parameter, one-bucket test model. Fewer dispatches alone do not establish faster training.
PyTorch: testing & review
Scratch memory
The figures come from my PR’s RTX 4090 benchmark and have not been rerun for this portfolio. The PR records correctness checks across ragged tensors and a uniform-input control. The transfer fix was part of the submitted work, not a change prompted by human review.
Batched copies
Review caught a correctness issue with higher-order gradients. The contribution retains individual copies for that case and batches ordinary copy-out. The landed commit credits a maintainer co-author; the PR discloses AI assistance.