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 is parallelized to use multiple cores for some operations. This restriction to a single-node CPU-only execution limit...

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Veröffentlicht in:SC19: International Conference for High Performance Computing, Networking, Storage and Analysis S. 1 - 23
Hauptverfasser: Bauer, Michael, Garland, Michael
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 17.11.2019
Schlagworte:
Arrays
Codes
Control Replication
Data models
Distributed Execution
GPU
HPC
Legate
Legion
Libraries
Logical Regions
NumPy
Programming
Python
Runtime
Scalability
Task-Based Runtimes
Throughput
Translation
ISSN:2167-4337
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Beschreibung
Zusammenfassung: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 is parallelized to use multiple cores for some operations. This restriction to a single-node CPU-only execution limits both the size of data that can be handled and the potential speed of NumPy code. In this work we introduce Legate, a drop-in replacement for NumPy that requires only a single-line code change and can scale up to an arbitrary number of GPU accelerated nodes. Legate works by translating NumPy programs to the Legion programming model and then leverages the scalability of the Legion runtime system to distribute data and computations across an arbitrary sized machine. Compared to similar programs written in the distributed Dask array library in Python, Legate achieves speed-ups of up to 10X on 1280 CPUs and 100X on 256 GPUs.
ISSN:2167-4337
DOI:10.1145/3295500.3356175