As massively parallel, distributed memory systems replace traditional vector supercomputers, effective application program optimization and system resource management become more than research curiosities --- they are crucial to achieving substantial fractions of peak performance for scientific application codes. By recording dynamic activity, either at the application or system software level, one can identify and remove performance bottlenecks. Pablo is a performance analysis environment designed to provide performance data capture, analysis, and presentation across a wide variety of scalable parallel systems. The Pablo environment includes software performance instrumentation, graphical performance data reduction and analysis, and support for mapping performance data to both graphics and sound. Current research directions include complete performance data immersion via head-mounted displays and the integration of Pablo with data parallel Fortran compilers based on the emerging High ...

Although the nascent state of parallel systems makes empirical performance measurement, analysis and tuning critical, rapid technological evolution, coupled with short product life cycles, has often made it difficult to isolate fundamental experimental principles from implementation artifacts. By definition, the apparatus for experimental performance analysis (i.e., instrumentation specification, data buffering, timestamp generation, and data extraction) is shaped by the intended experiment and the object of study. In some environments, certain experiments are not feasible. Balancing the volume of captured performance data against its accuracy and timeliness requires both appropriate tools and an understanding of instrumentation costs, implementation alternatives, and support infrastructure.