Optimizing and Scaling HPCG on Tianhe-2: Early Experience

摘要

In this paper, a first attempt has been made on optimizing and scaling HPCG on the world's largest supercomputer, Tianhe-2. This early work focuses on the optimization of the CPU code without using the Intel Xeon Phi coprocessors. In our work, we reformulate the basic CG algorithm to minimize the cost of collective communication and employ several optimizing techniques such as SIMDization, loop unrolling, forward and backward sweep fusion, OpenMP parallization to further enhance the performance of kernels such as the sparse matrix vector multiplication, the symmetric Gauss-Seidel relaxation and the geometric multigrid v-cycle. We successfully scale the HPCG code from 256 up to 6,144 nodes (147,456 CPU cores) on Tianhe-2, with a nearly ideal weak scalability and an aggregate performance of 79.83 Tflops, which is 6.38X higher than the reference implementation.