Profiling techniques for communication in fine-grained parallel languages

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 quite challenging to profile programs that make use of efficient, low-overhead communication. We incorporated techniques which minimize profiling effects on the running program, and quantified the profiling overhead. We present several Split-C applications showing that the profiler is useful in determining performance bottlenecks. Copyright (C) 1999 John Whey & Sons, Ltd.