Characterizing Loop-Level Communication Patterns in Shared Memory

Communication patterns extracted from parallel programs can provide a valuable source of information for parallel pattern detection, application auto-tuning, and runtime workload scheduling on heterogeneous systems. Once identified, such patterns can help find the most promising optimizations. Communication patterns can be detected using different methods, including sandbox simulation, memory profiling, and hardware counter analysis. However, these analyses usually suffer from high runtime and memory overhead, necessitating a trade off between accuracy and resource consumption. More importantly, none of the existing methods exploit fine-grained communication patterns on the level of individual code regions. In this paper, we present an efficient tool based on Disco PoP profiler that characterizes the communication pattern of every hotspot in a shared-memory application. With the aid of static and dynamic code analysis, it produces a nested structure of communication patterns based on program's loops. By employing asymmetric signature memory, the runtime overhead is around 225× while the required amount of memory remains fixed. In comparison with other profilers, the proposed method is efficient enough to be used with real world applications.