Efficient Low-Precision GEMM on Ascend NPU: HGEMM’s Synergy of Pipeline Scheduling, Tiling, and Memory Optimization
Erkun Zhang, Pengxiang Xu, Lu Lu · Computers · 2026
As one of the most widely used high-performance kernels, General Matrix Multiplication, or GEMM, plays a pivotal role in diverse application fields. With the growing prevalence of training for Convolutional Neural Networks (CNNs) and Large Language Models (LLMs), the design and implementation of high-efficiency, low-precision GEMM on modern Neural Processing Unit (NPU) platforms are of great significance. In this work, HGEMM for Ascend NPU is presented, which enables collaborative processing of different computation types by Cube units and Vector units. The major contributions of this work are the following: (i) dual-stream pipeline scheduling is implemented, which synchronizes padding operations, matrix–matrix multiplications, and element-wise instructions across hierarchical buffers and compute units; (ii) a suite of tiling strategies and a corresponding strategy selection mechanism are developed, comprehensively accounting for the impacts from M, N, and K directions; and (iii) SplitK as well as ShuffleK methods are raised to address the challenges of memory access efficiency and AI Core utilization. Extensive evaluations demonstrate that our proposed HGEMM achieves an average 3.56× speedup over the CATLASS template-based implementation under identical Ascend NPU configurations, and an average 2.10× speedup relative to the cuBLAS implementation on Nvidia A800 GPUs under general random workloads. It also achieves a maximum computational utilization exceeding 90% under benchmark workloads. Moreover, the proposed HGEMM not only significantly outperforms the CATLASS template-based implementation but also delivers efficiency comparable to the cuBLAS implementation in OPT-based bandwidth-limited LLM inference workloads.