计算机工程与科学2026，Vol.48Issue(6)：997-1007,11.DOI:10.3969/j.issn.1007-130X.2026.06.004
基于国产异构平台的光前哈密顿量对角化优化
Optimization of light-front Hamiltonian diagonalization on domestic heterogeneous platform
摘要
Abstract
To address the performance bottleneck encountered by the basis light-front quantization(BLFQ)method when solving the eigenvalue problems of large-scale sparse Hamiltonian matrices,this paper proposes an optimization scheme based on a domestic heterogeneous computing platform.This scheme employs a heterogeneous computing architecture that combines CPUs and GPU-like accelerator cards,utilizing message passing interface(MPI)for inter-process communication,offloading core com-putational tasks to the accelerator cards for execution.By porting the relevant interfaces of the AR-PACK library to make it compatible with the accelerator card environment,designs a parallel algorithm for sparse matrix-vector multiplication.The key improvements include replacing the original CPU-side linear algebra functions with accelerator-optimized functions,utilizing NVIDIA collective communication library(NCCL)for inter-accelerator communication,and adopting strategies such as thread coalescing memory access and load balancing to optimize parallel performance.Experimental results demonstrate that the optimization scheme achieves significant speedups across various test scales,with acceleration ratios ranging from 3.7 to 10.3.Additionally,communication overhead is significantly reduced,and load distribution becomes more balanced.The proposed scheme effectively enhances the performance of BLFQ in hadron physics computations and provides valuable insights for scientific computing applications involving large-scale sparse matrices in other domains.关键词
基矢光前量子化/哈密顿矩阵/稀疏矩阵-向量乘法/异构计算/通信优化/负载均衡/合并访存Key words
basis light-front quantization(BLFQ)/Hamiltonian matrix/sparse matrix-vector multi-plication(SpMV)/heterogeneous computing/communication optimization/load balancing/coalesced memory access分类
信息技术与安全科学引用本文复制引用
陶晨博,韩雨萌,武鹏,徐思琦,赵行波,刘杰,戴荣,曹武迪..基于国产异构平台的光前哈密顿量对角化优化[J].计算机工程与科学,2026,48(6):997-1007,11.基金项目
国家自然科学基金(12375143) （12375143）
国家重点研发计划(2021YFB0300200) （2021YFB0300200）
河南省重大科技专项(221100210600) （221100210600）
