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ExHetAI: Extreme Heterogeneity and AI Convergence in HPC
DescriptionThe increasing convergence of AI and HPC, combined with the rapid evolution of heterogeneous computing architectures, is transforming modern supercomputing. The emergence of specialized accelerators, including GPUs, TPUs, IPUs, neuromorphic chips, quantum processors, and FPGAs, has introduced new challenges in performance portability, system optimization, and software adaptability. In this exascale and extreme heterogeneity era, effectively exploiting diverse hardware architectures requires AI-driven approaches, novel programming models, and intelligent workload management. This workshop will bring together experts from academia, industry, and national laboratories to explore AI-HPC convergence, heterogeneous system architectures, energy-efficient computing, and AI-assisted performance optimization. By fostering interdisciplinary discussions and collaborations, the workshop aims to advance scalable, efficient, and sustainable computing. We invite contributions on topics including heterogeneous hardware, AI-driven HPC techniques, memory architectures, and programming models, with a focus on shaping the future of AI-driven scientific discovery and high performance computing.
