The ASPLOS 2025 / EuroSys 2025 Contest on Intra-Operator Parallelism for Distributed Deep Learning

A chief enabler of large-scale deep learning is the distribution of computation across multiple interconnected hardware accelerators. In order to unlock the maximum possible performance, a compiler must first select a reasonable strategy to parallelize a model's operations. Since neural network architectures admit multiple flavors of parallelism, determining the proper strategy for each instruction is a critical (albeit non-trivial) task. To solicit new ideas toward solving this challenging combinatorial optimization problem, we organized the ASPLOS 2025 / EuroSys 2025 Contest on Intra-Operator Parallelism for Distributed Deep Learning, a multi-month competition focused on advancing the state-of-the-art for model partitioning algorithms. In this paper, we offer a retrospective of this event, including the basic problem formulation, key challenges & opportunities, our new benchmark suite, and the quality of submissions received.