Accelerating Python Applications with Dask and ProxyStore

TL;DR

This paper explores integrating ProxyStore with Dask Distributed to enhance data flow efficiency, scalability, and performance in Python-based distributed scientific workflows, addressing existing limitations in data-intensive applications.

Contribution

It introduces a novel integration of ProxyStore with Dask Distributed, improving scalability and performance for data-intensive Python workflows.

Findings

Enhanced performance on synthetic benchmarks

Improved scalability for real applications

Effective data flow optimization

Abstract

Applications are increasingly written as dynamic workflows underpinned by an execution framework that manages asynchronous computations across distributed hardware. However, execution frameworks typically offer one-size-fits-all solutions for data flow management, which can restrict performance and scalability. ProxyStore, a middleware layer that optimizes data flow via an advanced pass-by-reference paradigm, has shown to be an effective mechanism for addressing these limitations. Here, we investigate integrating ProxyStore with Dask Distributed, one of the most popular libraries for distributed computing in Python, with the goal of supporting scalable and portable scientific workflows. Dask provides an easy-to-use and flexible framework, but is less optimized for scaling certain data-intensive workflows. We investigate these limitations and detail the technical contributions necessary to develop a robust solution for distributed applications and demonstrate improved performance on synthetic benchmarks and real applications.