Verifying Causality between Distant Performance Phenomena in Large-Scale MPI Applications

Abstract

In message-passing applications, the temporal or spatial distance between cause and symptom of a performance problem constitutes a major difficulty in deriving helpful conclusions from performance data. Just knowing the locations of wait states in the program is often insufficient to understand the reason for their occurrence. We present a method for verifying hypotheses on causality between temporally or spatially distant performance phenomena in message-passing applications without altering the application itself. The verification is accomplished by modifying MPI event traces and using them to simulate the hypothetical message-passing behavior. By performing a parallel real-time reenactment of the communication to be simulated using the original execution configuration, we can achieve high scalability and good predictive accuracy in relation to the measured behavior. Not relying on a potentially complex model of the message-passing subsystem, our method is also platform independent.