Abstract: Scientific applications are increasingly adopting Artificial Intelligence (AI) techniques to advance science. High performance computing centers are evaluating emerging novel hardware accelerators to efficiently run AI-driven science applications. With a wide diversity in the hardware architectures and software stacks of these systems, it is challenging to understand how these accelerators perform.

First, I will present an overview of the ALCF AI Testbed that houses novel AI accelerators from SambaNova, Cerebras, Graphcore, Groq, and Habana, with the goal to understand the efficiency and efficacy of these systems to accelerate AI for science applications. Next, I present a detailed evaluation of these accelerators with diverse workloads, such as Deep Learning (DL) primitives, benchmark models, and scientific machine learning applications along with performance of collective communications and scaling efficiencies. I will conclude with key insights, challenges, and opportunities in integrating these novel AI accelerators in supercomputing systems.