A Comprehensive Evaluation of Novel AI Accelerators for Deep Learning Workloads
DescriptionScientific 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. The state-of-the-art in the evaluation of deep learning workloads primarily focuses on CPUs and GPUs. In this paper, we present an overview of dataflow-based novel AI accelerators from SambaNova, Cerebras, Graphcore, and Groq.
We present a first-of-a-kind evaluation of these accelerators with a diverse set of workloads, such as deep learning (DL) primitives, benchmark models, and scientific machine learning applications. We also evaluate the performance of collective communication, which is key for distributed DL implementation, along with a study of scaling efficiency. We then discuss key insights, challenges, and opportunities in integrating these novel AI accelerators in supercomputing systems.
We present a first-of-a-kind evaluation of these accelerators with a diverse set of workloads, such as deep learning (DL) primitives, benchmark models, and scientific machine learning applications. We also evaluate the performance of collective communication, which is key for distributed DL implementation, along with a study of scaling efficiency. We then discuss key insights, challenges, and opportunities in integrating these novel AI accelerators in supercomputing systems.
Event Type
Workshop
TimeMonday, 14 November 20229am - 9:30am CST
LocationC155
W
Applications
Architectures
Benchmarking
Exascale Computing
Modeling and Simulation
Performance
Performance Portability
Recorded
Author/Presenters
