Papers › GPU Kernels for Block-Sparse Weights
GPU Kernels for Block-Sparse Weights
Scott Gray, Alec Radford and Diederik P. Kingma
We’re releasing highly optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. The kernels allow for efficient evaluation and differentiation of linear layers, including convolutional layers, with flexibly configurable block-sparsity patterns in the weight matrix. We find that depending on the sparsity, these kernels can run orders of magnitude faster than the best available alternatives such as cuBLAS. Using the kernels we improve upon the state-of-the-art in text sentiment analysis and generative modeling of text and images. By releasing our kernels in the open we aim to spur further advancement in model and algorithm design.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | CR | Block-sparse LSTM | Accuracy | 92.2 | #4 of 9 | Archive leaderboard | report |
| Sentiment Analysis | IMDb | Block-sparse LSTM | Accuracy | 94.99 | #19 of 49 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | Block-sparse LSTM | Accuracy | 93.2 | #41 of 87 | Archive leaderboard | report |
| Sentiment Analysis | Yelp Binary classification | Block-sparse LSTM | Error | 3.27 | #11 of 20 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections