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