Deep start: A hybrid strategy for automated performance problem searches

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.