LP-GEMM: Integrating Layout Propagation into GEMM Operations
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 restore outputs to a canonical memory layout. In sequential GEMMs, these constraints cause redundant packing and unpacking, wasting valuable computational resources. This paper introduces LP-GEMM, a decomposition of the GEMM kernel that enables packing-layout propagation across sequential GEMM operations. This approach eliminates unnecessary da...
| Authors | César Guedes Carneiro, Lucas Alvarenga, Guido Araujo et al. |
|---|---|
| Primary category | cs.DC |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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