LP-GEMM: Integrating Layout Propagation into GEMM Operations

• URL: http://arxiv.org/abs/2604.04599v1
• Date: Mon, 06 Apr 2026 11:24:34 GMT
• Title: LP-GEMM: Integrating Layout Propagation into GEMM Operations
• Abstract summary: This paper introduces LP-GEMM, a decomposition of the GEMM kernel that enables packing- propagation across sequential GEMM operations.<n>We show average speedups of 2.25x over OpenBLA on Intel x86 for sequential GEMMs and competitive gains relative to vendor-optimized libraries.
• Score: 0.20388938295521575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 data repacking while preserving full BLAS semantic correctness at the boundaries. We evaluate LP-GEMM on x86 (AVX-512) and RISC-V (RVV 1.0) architectures across MLP-like and Attention-like workloads. Our results show average speedups of 2.25x over OpenBLAS on Intel x86 for sequential GEMMs and competitive gains relative to vendor-optimized libraries such as Intel MKL. We demonstrate the practicality of the approach beyond microbenchmarks by implementing a standalone C++ version of the Llama-3.2 inference path using exclusively BLAS-level GEMM calls. These results confirm that leveraging data layout propagation between operations can significantly boost performance.

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