Automatic Parallelization of Python Programs for Distributed Heterogeneous Computing

TL;DR

This paper presents an automated approach to transform sequential Python code into highly optimized parallel code for distributed heterogeneous systems, leveraging type hints, polyhedral optimizations, and Ray runtime, achieving massive performance gains.

Contribution

It introduces a novel AOT source-to-source transformation framework for Python that automates parallelization and optimization for heterogeneous distributed hardware.

Findings

Achieved over 20,000× speedup on supercomputers with 24 nodes and 144 GPUs.

Enabled automatic CPU/GPU code variant selection and high-level loop optimizations.

Demonstrated significant performance improvements in real-world applications.

Abstract

This paper introduces a novel approach to automatic ahead-of-time (AOT) parallelization and optimization of sequential Python programs for execution on distributed heterogeneous platforms. Our approach enables AOT source-to-source transformation of Python programs, driven by the inclusion of type hints for function parameters and return values. These hints can be supplied by the programmer or obtained by dynamic profiler tools; multi-version code generation guarantees the correctness of our AOT transformation in all cases. Our compilation framework performs automatic parallelization and sophisticated high-level code optimizations for the target distributed heterogeneous hardware platform. It includes extensions to the polyhedral framework that unify user-written loops and implicit loops present in matrix/tensor operators, as well as automated section of CPU vs. GPU code variants. Further, our polyhedral optimizations enable both intra-node and inter-node parallelism. Finally, the optimized output code is deployed using the Ray runtime for scheduling distributed tasks across multiple heterogeneous nodes in a cluster. Our empirical evaluation shows significant performance improvements relative to sequential Python in both single-node and multi-node experiments, with a performance improvement of over 20,000$\times$ when using 24 nodes and 144 GPUs in the OLCF Summit supercomputer for the Space-Time Adaptive Processing (STAP) radar application.