Profiling Techniques for a Fine-Grained Parallel Language

Fine tuning the performance of large parallel programs is a very difficult task. A profiling tool can provide detailed insight into the utilization and communication of the different processors, which helps identify performance bottlenecks. In this paper we present two profiling techniques for the fine-grained parallel programming language Split-C, which provides a simple global address space memory model. One profiler provides a detailed analysis of a program’s execution. The other profiler collects cumulative information. As our experience shows, it is much more challenging to profile programs that make use of efficient, low-overhead communication. We incorporated techniques which minimize profiling effects on the running program. We quantify the profiling overhead and present several Split-C applications which show that the profiler is useful in determining performance bottlenecks.