AI integration is revolutionizing the landscape of HPC simulations, enhancing the importance, use, and performance of AI-driven HPC workflows. This paper surveys the diverse and rapidly evolving field of AI-driven HPC and provides a common conceptual basis for understanding AI-driven HPC workflows. Specifically, we use insights from different modes of coupling AI into HPC workflows to propose six execution motifs most commonly found in scientific applications. The proposed set of execution motifs is by definition incomplete and evolving. However, they allow us to analyze the primary performance challenges underpinning AI-driven HPC workflows. We close with a listing of open challenges, research issues, and suggested areas of investigation including the the need for specific benchmarks that will help evaluate and improve the execution of AI-driven HPC workflows.

翻译：人工智能的集成正在彻底改变高性能计算模拟的格局，提升了AI驱动的高性能计算工作流的重要性、使用率和性能。本文综述了AI驱动的高性能计算这一多样化且快速发展的领域，并为理解AI驱动的高性能计算工作流提供了一个共同的概念基础。具体而言，我们利用将AI耦合到高性能计算工作流的不同模式中的见解，提出了科学应用中最常见的六种执行模式。所提出的执行模式集合在定义上是不完整且不断发展的。然而，它们使我们能够分析支撑AI驱动的高性能计算工作流的主要性能挑战。最后，我们列举了包括需要特定基准测试在内的开放挑战、研究问题及建议的研究方向，这些基准测试将有助于评估和改进AI驱动的高性能计算工作流的执行。