This article explains how to efficiently multiply matrices when their parameters are distributed across multiple accelerators like TPUs and GPUs. It introduces a notation system using device meshes and sharding assignments to describe how tensor dimensions are partitioned across physical hardware, enabling scalable training of large language models.
ProofWatch discusses rumors and sources behind claims of breakthroughs in mathematics and computer science, with a source commenting on potential minor improvements to matrix multiplication algorithms.