We can profile the performance behavior of parallel programs at the level of individual call paths through sampling or direct instrumentation. While we can easily control measurement dilation by adjusting the sampling frequency, the statistical nature of sampling and the difficulty of accessing the parameters of sampled events make it unsuitable for obtaining certain communication metrics, such as the size of message payloads. Alternatively, direct instrumentation, which is preferable for capturing message-passing events, can excessively dilate measurements, particularly for C++ programs, which often have many short but frequently called class member functions. Thus, we combine these techniques in a unified framework that exploits the strengths of each approach while avoiding their weaknesses: We use direct instrumentation to intercept MPI routines while we record the execution of the remaining code through low-overhead sampling. One of the main technical hurdles mastered was the inexpensive and portable determination of call-path information during the invocation of MPI routines. We show that the overhead of our implementation is sufficiently low to support substantial performance improvement of a C++ fluid-dynamics code.

The performance behavior of parallel simulations often changes considerably as the simulation progresses --- with potentially process-dependent variations of temporal patterns. While call-path profiling is an established method of linking a performance problem to the context in which it occurs, call paths reveal only little information about the temporal evolution of performance phenomena. However, generating call-path profiles separately for thousands of iterations may exceed available buffer space --- especially when the call tree is large and more than one metric is collected. In this paper, we present a runtime approach for the semantic compression of call-path profiles based on incremental clustering of a series of single-iteration profiles that scales in terms of the number of iterations without sacrificing important performance details. Our approach offers low runtime overhead by using only a condensed version of the profile data when calculating distances and accounts for process-dependent variations by making all clustering decisions locally.