Asynchronous execution of python code on task-based runtime systems

R. Tohid, Bibek Wagle, Shahrzad Shirzad, Patrick Diehl, Adrian Serio, Alireza Kheirkhahan, Parsa Amini, Katy Williams, Kate Isaacs, Kevin Huck, Steven Brandt, Hartmut Kaiser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Despite 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.

Original languageEnglish (US)
Title of host publicationProceedings of ESPM2 2018
Subtitle of host publication4th International Workshop on Extreme Scale Programming Models and Middleware, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages37-45
Number of pages9
ISBN (Electronic)9781728101781
DOIs
StatePublished - Feb 8 2019
Event4th IEEE/ACM International Workshop on Extreme Scale Programming Models and Middleware, ESPM2 2018 - Dallas, United States
Duration: Nov 12 2018 → …

Publication series

NameProceedings of ESPM2 2018: 4th International Workshop on Extreme Scale Programming Models and Middleware, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis

Conference

Conference4th IEEE/ACM International Workshop on Extreme Scale Programming Models and Middleware, ESPM2 2018
CountryUnited States
CityDallas
Period11/12/18 → …

Keywords

  • Array-computing
  • Asynchronous
  • HPX
  • High-Performance-Computing
  • Python
  • Runtime-systems

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

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  • Cite this

    Tohid, R., Wagle, B., Shirzad, S., Diehl, P., Serio, A., Kheirkhahan, A., Amini, P., Williams, K., Isaacs, K., Huck, K., Brandt, S., & Kaiser, H. (2019). Asynchronous execution of python code on task-based runtime systems. In Proceedings of ESPM2 2018: 4th International Workshop on Extreme Scale Programming Models and Middleware, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 37-45). [8638482] (Proceedings of ESPM2 2018: 4th International Workshop on Extreme Scale Programming Models and Middleware, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ESPM2.2018.00009