ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling

• URL: http://arxiv.org/abs/2606.10440v1
• Date: Tue, 09 Jun 2026 05:36:31 GMT
• Title: ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling
• Abstract summary: Distributed machine learning (ML) is a key paradigm for today's large-scale artificial intelligence applications.<n>We revisit the promising open-source, community-driven simulator: ASTRA-sim.<n>InfraGraph is a standardized representation to capture distributed ML network infrastructure in detail.
• Score: 4.001628457251908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributed machine learning (ML) is a key paradigm for today's large-scale artificial intelligence applications. As model inference arises as an important use case, faithful modeling of latency-sensitive collective communication has never been more important. Capturing the device architecture and modeling control and data paths at high fidelity is therefore a necessity today. Having a common, detailed representation for distributed ML infrastructure is also crucial. We revisit the promising open-source, community-driven simulator: ASTRA-sim. In this work, we identify limitations of the current ASTRA-sim simulator and augment it with new features. To this end, we enable fine-grained, high-fidelity simulation with a standardized infrastructure representation, opening new design space exploration opportunities. We propose the simulation at cache-line-sized load-store granularity, with a detailed graphics processing unit (GPU) execution model, to balance simulation scalability and fidelity. We also introduce InfraGraph, a standardized representation to capture distributed ML network infrastructure in detail. Using the updated ASTRA-sim 3.0 simulator, we showcase interesting design space explorations for designing optimized collective algorithms, network requirements, and GPU architectures.

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