Parallel Loop Locality Analysis for Symbolic Thread Counts

Fangzhou Liu, Yifan Zhu, Shaotong Sun, Chen Ding, Wesley Smith, Kaave Seyed Hosseini

Abstract: Data movement limits program performance. This bottleneck is more significant in multi-thread programs but more difficult to analyze, especially for multiple thread counts.For regular loop nests parallelized by OpenMP, this paper presents a new technique that predicts their miss ratio in the shared cache. It uses two statistical models, one for cache sharing and one for data sharing. Both models use a symbolic number of threads, making it trivial to compute the miss ratio of any additional thread count after initial analysis.The technique is implemented in a tool called PLUSS. When tested on 73 parallel loops used in scientific kernels, image processing and machine learning, PLUSS produces accurate results compared to profiling and reduces the analysis cost by up to two orders of magnitude.