AutoPerf: A Generalized Zero-Positive Learning System to Detect Software Performance Anomalies

In this paper, we present AutoPerf, a generalized software performance anomaly detection system. AutoPerf uses autoencoders, an unsupervised learning technique, and hardware performance counters to learn the performance signatures of parallel programs. It then uses this knowledge to identify when newer versions of the program suffer performance penalties, while simultaneously providing root cause analysis to help programmers debug the program's performance. AutoPerf is the first zero-positive learning performance anomaly detector, a system that trains entirely in the negative (non-anomalous) space to learn positive (anomalous) behaviors. We demonstrate AutoPerf's generality against three different types of performance anomalies: (i) true sharing cache contention, (ii) false sharing cache contention, and (iii) NUMA latencies across 15 real world performance anomalies and 7 open source programs. AutoPerf has only 3.7% profiling overhead (on average) and detects more anomalies than the prior state-of-the-art approach.