AI RESEARCH
LP-GEMM: Integrating Layout Propagation into GEMM Operations
arXiv CS.LG
•
ArXi:2604.04599v1 Announce Type: cross In Scientific Computing and modern Machine Learning (ML) workloads, sequences of dependent General Matrix Multiplications (GEMMs) often dominate execution time. While state-of-the-art BLAS libraries aggressively optimize individual GEMM calls, they remain constrained by the BLAS API, which requires each call to independently pack input matrices and re outputs to a canonical memory layout. In sequential GEMMs, these constraints cause redundant packing and unpacking, wasting valuable computational resources.