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This paper presents a novel heartbeat diagnosis regarding performance anomalyfor OpenMP multi-threaded applications. First, we design injected heartbeatAPIs for OpenMP multi-threaded applications. Then, we leverage the heartbeatsequences to extract features of previously-observed anomalies. Finally, weadopt a tree-based algorithm, namely HSA, to identify the features that arerequired to diagnose anomalies. To evaluate our framework, the NAS Parallel NPBbenchmark, EPCC OpenMP micro-benchmark suite, and Jacobi benchmark are used totest the performance of our approach proposed.

Heartbeat Diagnosis of Performance Anomaly in OpenMP Multi-Threaded Systems