Efficient Reuse Distance Analysis of Multicore Scaling for Loop-Based Parallel Programs

摘要

Reuse Distance (RD) analysis is a powerful memory analysis tool that can potentially help architects study multicore processor scaling. One key obstacle, however, is that multicore RD analysis requires measuring Concurrent Reuse Distance (CRD) and Private-LRU-stack Reuse Distance (PRD) profiles across thread-interleaved memory reference streams. Sensitivity to memory interleaving makes CRD and PRD profiles architecture dependent, preventing them from analyzing different processor configurations. For loop-based parallel programs, CRD and PRD profiles shift coherently across RD values with core count scaling because interleaving threads are symmetric. Simple techniques can predict such shifting, making the analysis of numerous multicore configurations from a small set of CRD and PRD profiles feasible. Given the ubiquity of parallel loops, such techniques will be extremely valuable for studying future large multicore designs. This article investigates using RD analysis to efficiently analyze multicore cache performance for loop-based parallel programs, making several contributions. First, we provide an in-depth analysis on how CRD and PRD profiles change with core count scaling. Second, we develop techniques to predict CRD and PRD profile scaling, in particular employing reference groups [Zhong et al. 2003] to predict coherent shift, demonstrating 90% or greater prediction accuracy. Third, our CRD and PRD profile analyses define two application parameters with architectural implications: C_(core) is the minimum shared cache capacity that "contains" locality degradation due to core count scaling, and C_(share) is the capacity at which shared caches begin to provide a cache-miss reduction compared to private caches. And fourth, we apply CRD and PRD profiles to analyze multicore cache performance. When combined with existing problem scaling prediction, our techniques can predict shared LLC MPKI (private L2 cache MPKI) to within 10.7% (13.9%) of simulation across 1,728 (1,440) configurations using only 36 measured CRD (PRD) profiles.