ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling
arXiv:2606.10440v1 Announce Type: cross 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. I
The increasing scale and complexity of distributed machine learning models necessitate higher fidelity simulation tools to optimize performance and resource utilization.
Advanced simulation capabilities like ASTRA-sim 3.0 are critical for designing efficient and latency-sensitive distributed AI systems, impacting training costs and inference speeds for large AI models.
The ability to accurately model GPU and infrastructure interactions at a granular level allows for more precise architectural decisions and performance predictions in distributed AI system design.
- · AI hardware developers
- · Hyperscalers
- · Distributed ML researchers
- · Chip manufacturers
- · Inefficient AI infrastructure designs
- · Developers relying on heuristic-based optimizations
Improved performance and reduced development cycles for large-scale distributed AI applications.
Accelerated innovation in AI model architectures and training techniques due to better system understanding.
Potentially democratized access to high-performance distributed AI due to more optimized and cost-effective deployments.
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Read at arXiv cs.LG