Asynchronous Execution of Python Code on Task Based Runtime Systems

TL;DR

This paper introduces Phylanx, a toolkit that enables high-level Python and NumPy code to run efficiently in parallel and distributed environments on HPC systems, bridging ease of programming with performance.

Contribution

It presents a novel approach to execute Python and NumPy operations asynchronously on HPC resources using a dependency tree mapped onto the HPX runtime system.

Findings

Achieved parallel execution of machine learning algorithms in Python.

Demonstrated performance comparable to standard NumPy implementations.

Provided debugging and visualization tools for performance analysis.

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.