Task-Based Algorithm for Matrix Multiplication: A Step Towards Block-Sparse Tensor Computing

Understand

Distributed-memory matrix multiplication (MM) is a key element of algorithms in many domains (machine learning, quantum physics).

• Conventional algorithms for dense MM rely on regular/uniform data decomposition to ensure load balance.
• These traits conflict with the irregular structure (block-sparse or rank-sparse within blocks) that is increasingly relevant for fast methods in quantum physics.
• To deal with such irregular data we present a new MM algorithm based on Scalable Universal Matrix Multiplication Algorithm (SUMMA).