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.
@article{arxiv.2103.10635,
title = {PPT-SASMM: Scalable Analytical Shared Memory Model: Predicting the Performance of Multicore Caches from a Single-Threaded Execution Trace},
author = {Atanu Barai and Gopinath Chennupati and Nandakishore Santhi and Abdel-Hameed Badawy and Yehia Arafa and Stephan Eidenbenz},
journal= {arXiv preprint arXiv:2103.10635},
year = {2021}
}
Comments
11 pages, 5 figures. arXiv admin note: text overlap with arXiv:1907.12666