  Some OpenMP multi-threaded applications increasingly suffer from performance
anomaly owning to shared resource contention as well as software- and
hardware-related problems. Such performance anomaly can result in failure and
inefficiencies, and are among the main challenges in system resiliency. To
minimize the impact of performance anomaly, one must quickly and accurately
detect and diagnose the performance anomalies that cause the failures. However,
it is difficult to identify anomalies in the dynamic and noisy data collected
by OpenMP multi-threaded monitoring infrastructures. This paper presents a
novel machine learning framework for performance anomaly in OpenMP
multi-threaded systems. To evaluate our framework, the NAS Parallel NPB
benchmark, EPCC OpenMP micro-benchmark suite, and Jacobi benchmark are used to
test the performance of our framework proposed. The experimental results
demonstrate that our framework successfully identifies 90.3\% of injected
anomalies of OpenMP multi-threaded applications.
