ScaAnalyzer: A Tool to Identify Memory Scalability Bottlenecks in Parallel Programs

It is difficult to scale parallel programs in a system that employs a large number of cores. To identify scalability bottlenecks, existing tools principally pinpoint poor thread synchronization strategies or unnecessary data communication. Memory subsystem is one of the key contributors to poor parallel scaling in multicore machines. State-of-theart tools, however, either lack sophisticated capabilities or are completely ignorant in pinpointing scalability bottlenecks arising from the memory subsystem. To address this issue, we develop a tool ScaAnalyzer to pinpoint scaling losses due to poor memory access behaviors of parallel programs. ScaAnalyzer collects, attributes, and analyzes memory-related metrics during program execution while incurring very low overhead. ScaAnalyzer provides high-level, detailed guidance to programmers for scalability optimization. We demonstrate the utility of ScaAnalyzer with case studies of three parallel programs. For each benchmark, ScaAnalyzer identifies scalability bottlenecks caused by poor memory access behaviors and provides optimization guidance that yields significant improvement in scalability.