Workshops Archive

Concurrency Patterns and Primitives in Modern AI/ML Scientific Applications

Workshop: WORKS 2025: 20th Workshop on Workflows in Support of Large-Scale Science

Authors: Nicholson Collier and Amal Gueroudji (Argonne National Laboratory (ANL)), Mihael Hategan-Marandiuc (University of Chicago), and Jonathan Ozik and Justin Wozniak (Argonne National Laboratory (ANL))

Abstract: While increases in available hardware concurrency have been the primary area of performance improvement over the last decade or so, parallel/concurrent programming is still a challenge. Most mainstream programming approaches, languages, and systems are designed for sequential programming first, with concurrency an afterthought. This poses a challenge for modern workloads, especially in areas such as artificial intelligence, machine learning, and data analytics, where there is an abundance of irregular concurrency due to unbalanced workloads and I/O patterns. Additionally, concurrency bugs tend to be nondeterministic, difficult to trace/reproduce, and consequently under-reported.

In this position paper, we describe the state-of-the-art in workflow-level concurrency, the challenges and opportunities in emerging application areas, and outline a solution in the form of a novel Python-based programming model.

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