In this paper, we present a new technique called dynamic instrumentation that provides efficient, scalable, yet detailed data collection for large-scale parallel applications. Our approach is unique because it defers inserting any instrumentation until the application is in execution. We can insert or change instrumentation at any time during execution by modifying the application’s binary image. Only the instrumentation required for the currently selected analysis or visualization is inserted. As a result, our technique collects several orders of magnitude less data than traditional data collection approaches. We have implemented a prototype of our dynamic instrumentation on the CM-5, and present results for several real applications. In addition, we include recommendations to operating system designers, compiler writers, and computer architects about the features necessary to permit efficient monitoring of large-scale parallel systems. 1
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