2020/09/28 by Atanu Barai, Gopinath Chennupati, Nandakishore Santhi +4 · 1 citation
Computer Science · #Advanced Data Storage Technologies #Block (permutation group theory) #CPU cache #Cache #Cache coloring #Cache-only memory architecture #Computer architecture #Computer science #Distributed computing #Interconnection Networks and Systems #Multi-core processor #Operating system #Parallel Computing and Optimization Techniques #Parallel computing #Probabilistic logic #Reuse #Scalability #Shared memory #cs.DC #cs.PF
paper · pdf · open access · doi:10.1145/3422575.3422806
11 pages, 5 figures. arXiv admin note: text overlap with arXiv:1907.12666
openalex publication_date 2020/09/28 · arxiv created 2021/03/19 · arxiv updated 2021/03/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Performance modeling of parallel applications on multicore processors remains a challenge in computational co-design due to multicore processors’ complex design. Multicores include complex private and shared memory hierarchies. We present a Scalable Analytical Shared Memory Model (SASMM). SASMM can predict the performance of parallel applications running on a multicore. SASMM uses a probabilistic and computationally-efficient method to predict the reuse distance profiles of caches in multicores. SASMM relies on a stochastic, static basic block-level analysis of reuse profiles. The profiles are calculated from the memory traces of applications that run sequentially rather than using multi-threaded traces. The experiments show that our model can predict private L1 cache hit rates with 2.12% and shared L2 cache hit rates with about 1.50% error rate.