Asynchronous Execution of Python Code on Task-Based Runtime Systems
MetadataShow full item record
CitationR. Tohid et al., "Asynchronous Execution of Python Code on Task-Based Runtime Systems," 2018 IEEE/ACM 4th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2), Dallas, TX, USA, 2018, pp. 37-45. doi: 10.1109/ESPM2.2018.00009
JournalPROCEEDINGS OF 2018 IEEE/ACM 4TH INTERNATIONAL WORKSHOP ON EXTREME SCALE PROGRAMMING MODELS AND MIDDLEWARE (ESPM2 2018)
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractDespite advancements in the areas of parallel and distributed computing, the complexity of programming on High Performance Computing (HPC) resources has deterred many domain experts, especially in the areas of machine learning and artificial intelligence (AI), from utilizing performance benefits of such systems. Researchers and scientists favor high-productivity languages to avoid the inconvenience of programming in low-level languages and costs of acquiring the necessary skills required for programming at this level. In recent years, Python, with the support of linear algebra libraries like NumPy, has gained popularity despite facing limitations which prevent this code from distributed runs. Here we present a solution which maintains both high level programming abstractions as well as parallel and distributed efficiency. Phylanx, is an asynchronous array processing toolkit which transforms Python and NumPy operations into code which can be executed in parallel on HPC resources by mapping Python and NumPy functions and variables into a dependency tree executed by HPX, a general purpose, parallel, task-based runtime system written in C++. Phylanx additionally provides introspection and visualization capabilities for debugging and performance analysis. We have tested the foundations of our approach by comparing our implementation of widely used machine learning algorithms to accepted NumPy standards.
VersionFinal accepted manuscript
SponsorsNSF Phylanx project