Legate NumPy: Accelerated and Distributed Array Computing

NumPy is a popular Python library used for performing array-based numerical computations. The canonical implementation of NumPy used by most programmers runs on a single CPU core and only a few operations are parallelized across cores. This restriction to single-node CPU-only execution limits both the size of data that can be processed and the speed with which problems can be solved. In this paper we introduce Legate, a programming system that transparently accelerates and distributes NumPy programs to machines of any scale and capability typically by changing a single module import statement. Legate achieves this by translating the NumPy application interface into the Legion programming model and leveraging the performance and scalability of the Legion runtime. We demonstrate that Legate can achieve state-of-the-art scalability when running NumPy programs on machines with up to 1280 CPU cores and 256 GPUs, allowing users to prototype on their desktop and immediately scale up to significantly larger machines. Furthermore, we demonstrate that Legate can achieve between one and two orders of magnitude better performance than the popular Python library Dask Array when running comparable programs at scale.

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