Computational Model and Algorithms for Tensor Core Units

Explore the theoretical foundations and practical implementations of tensor core units in this 35-minute conference talk that presents a comprehensive computational model for these specialized hardware accelerators. Learn about the mathematical frameworks that govern tensor core operations and discover algorithmic approaches designed to optimize their performance in parallel computing environments. Examine how tensor cores handle matrix multiplication and other linear algebra operations at scale, with particular focus on the computational models that describe their behavior and efficiency characteristics. Understand the algorithmic strategies developed to maximize throughput and minimize latency when working with tensor processing units, including considerations for memory hierarchy, data movement, and parallel execution patterns. Gain insights into the intersection of computer architecture and algorithm design as it applies to modern AI and machine learning workloads that rely heavily on tensor computations.