Democratizing AI Accelerators for HPC Applications:
Challenges, Success, and Support

Welcome

Recently, there has been a surge in the number of novel AI hardware architectures from companies including Cerebras, Groq, Tenstorrent, and others. Some of these systems are accessible to the research community through testbeds in the US (e.g. ALCF AI Testbed) and Europe (e.g. EPCC at the University of Edinburgh). Beyond their primary use in AI, these accelerators have sparked intense interest and rapid development in their capacity for handling HPC workloads and driving algorithmic research. Their distinctive architectural designs, often with significant raw compute capability and high bandwidth, have proven capable of significantly enhancing real-world performance for specific workloads, often outperforming GPUs. As these technologies are still emerging, the maturity levels of associated tooling and software stacks vary considerably, and there are gaps in documentation and support.

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💡 Topics for Discussion

This BoF aims to explore trends in AI accelerators for HPC, ideal programming models, software stack support, code portability, training resources, and common challenges. We will explore several pertinent questions:

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