SCP: Shared Cache Partitioning for High-Performance GEMM

GEneral Matrix Multiply (GEMM) is the most fundamental computational kernel routine in the BLAS library. To achieve high performance, in-memory data must be prefetched into fast on-chip caches before they are used. Two techniques, software prefetching and data packing, have been used to effectively exploit the capability of on-chip least recent used (LRU) caches, which are popular in traditional high-performance processors used in high-end servers and supercomputers. However, the market has recently witnessed a new diversity in processor design, resulting in high-performance processors equipped with shared caches with non-LRU replacement policies. This poses a challenge to the development of high-performance GEMM in a multithreaded context. As several threads try to load data into a shared cache simultaneously, interthread cache conflicts will increase significantly. We present a Shared Cache Partitioning (SCP) method to eliminate interthread cache conflicts in the GEMM routines, by partitioning a shared cache into physically disjoint sets and assigning different sets to different threads. We have implemented SCP in the OpenBLAS library and evaluated it on Phytium 2000+, a 64-core AArch64 processor with private LRU L1 caches and shared pseudo-random L2 caches (per four-core cluster). Our evaluation shows that SCP has effectively reduced the conflict misses in both L1 and L2 caches in a highly optimized GEMM implementation, resulting in an improvement of its performance by 2.75% to 6.91%.