Abstract
We present Deep Start, a new algorithm for automated performance diagnosis that uses stack sampling to augment our search-based automated performance diagnosis strategy. Our hybrid approach locates performance problems more quickly and finds problems hidden from a more straightforward search strategy. Deep Start uses stack samples collected as a by-product of normal search instrumentation to find deep starters, functions that are likely to be application bottlenecks. Deep starters are examined early during a search to improve the likelihood of finding performance problems quickly.We implemented the Deep Start algorithm in the Performance Consultant, Paradyn’s automated bottleneck detection component. Deep Start found half of our test applications’ known bottlenecks 32% to 59% faster than the Performance Consultant’s current call graphbased search strategy, and finished finding bottlenecks 10% to 61% faster. In addition to improving search time, Deep Start often found more bottlenecks than the call graph search strategy.
This work is supported in part by Department of Energy Grant DE-FG02-93ER25176, Lawrence Livermore National Lab grant B504964, and NSF grants CDA-9623632 and EIA-9870684. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.
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Roth, P.C., Miller, B.P. (2002). Deep Start: A Hybrid Strategy for Automated Performance Problem Searches. In: Monien, B., Feldmann, R. (eds) Euro-Par 2002 Parallel Processing. Euro-Par 2002. Lecture Notes in Computer Science, vol 2400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45706-2_9
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