Summary of Scorch: a Library For Sparse Deep Learning, by Bobby Yan et al.
Scorch: A Library for Sparse Deep Learning
by Bobby Yan, Alexander J. Root, Trevor Gale, David Broman, Fredrik Kjolstad
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Mathematical Software (cs.MS); Programming Languages (cs.PL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Scorch, a library that seamlessly integrates efficient sparse tensor computation into the PyTorch ecosystem, focusing on inference workloads on CPUs. Scorch provides a flexible interface for sparse tensors, supporting diverse data structures, and automates key optimizations such as loop ordering, tiling, and format inference. The library delivers substantial speedups over hand-written PyTorch Sparse operations without sacrificing usability, enabling efficient computation of complex sparse operations that lack hand-optimized implementations. This flexibility is crucial for exploring novel sparse architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scorch is a new library that helps make big neural networks run faster on computers. It’s like a shortcut that makes it easier to work with special types of data called “sparse tensors”. Scorch is part of the PyTorch system, which is used by many researchers and developers. With Scorch, people can make their code run up to 5 times faster without having to rewrite most of it. This will help scientists explore new ideas in deep learning and make big networks run more smoothly. |
Keywords
» Artificial intelligence » Deep learning » Inference
