PPT-SASMM：可扩展的分析共享内存模型：从单线程执行跟踪预测多核缓存的性能
PPT-SASMM: Scalable Analytical Shared Memory Model: Predicting the Performance of Multicore Caches from a Single-Threaded Execution Trace
摘要：
由于多核处理器设计的复杂性，多核处理器上并行应用的性能建模一直是计算协同设计中的一个难题。多核包括复杂的私有和共享存储层次结构。我们提出了一个可扩展的分析共享内存模型（SASMM）。SASMM可以预测在多核上运行的并行应用程序的性能。SASMM使用概率和计算效率高的方法来预测多核缓存的重用距离配置文件。SASMM依赖于对重用配置文件的随机、静态的基本块级分析。配置文件是根据顺序运行的应用程序的内存跟踪计算的，而不是使用多线程跟踪。实验结果表明，该模型可以预测私有L1缓存命中率为2.12%，共享L2缓存命中率为1.50%左右的出错率。
英文摘要：
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.
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