Abstract

The sparse module of the popular SciPy Python library is widely used across applications in scientific computing, data analysis, and machine learning. The standard implementation of SciPy is restricted to a single CPU and cannot take advantage of modern distributed and accelerated computing resources. We introduce Legate Sparse, a system that transparently distributes and accelerates unmodified sparse matrix-based SciPy programs across clusters of CPUs and GPUs, and composes with cuNumeric, a distributed NumPy library. Legate Sparse uses a combination of static and dynamic techniques to performantly compose independently written sparse and dense array programming libraries, providing a unified Python interface for distributed sparse and dense array computations. We show that Legate Sparse is competitive with single-GPU libraries like CuPy and the industry-standard PETSc library on up to 1280 CPU cores and 192 GPUs of the Summit supercomputer, while offering the productivity benefits of idiomatic SciPy and NumPy.

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BibTeX

@article{yadav2023,
  title={Legate Sparse: Distributed Sparse Computing in Python},
  author={Rohan Yadav and Wonchan Lee and Melih Elibol and Manolis Papadakis and Taylor Lee-Patti and Michael Garland and Alex Aiken and Fredrik Kjolstad and Michael Bauer},
  journal={ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis},
  year={2023},
  month={November}
}