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Profiling a Parallel Language Based on Fine-Grained Communication

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3 Author(s)
Haake, B. ; University of California, Santa Barbara ; Schauser, K.E. ; Scheiman, C.J.

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 a profiler for the fine-grained parallel programming language Split-C, which provides a simple global address space memory model. 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.

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Supercomputing, 1996. Proceedings of the 1996 ACM/IEEE Conference on

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