Abstract
With the growing complexity of supercomputing applications and systems, it is important to constantly develop existing performance measurement and analysis tools to provide new insights into application performance characteristics and thereby help scientists and engineers utilize computing resources more efficiently. We present the various new techniques developed, implemented and integrated into the Scalasca toolset specifically to enhance performance analysis of long-running applications. The first is a hybrid measurement system seamlessly integrating sampled and event-based measurements capable of low-overhead, highly detailed measurements and therefore particularly convenient for initial performance analyses. Then we apply iteration profiling to scientific codes, and present an algorithm for reducing the memory and space requirements of the collected data using iteration profile clustering. Finally, we evaluate the complete integration of all these techniques in a unified measurement system.
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