Dynamic statistical profiling of communication activity in distributed applications

Performance analysis of communication activity for a terascale application with traditional message tracing can be overwhelming in terms of overhead, perturbation, and storage. We propose a novel alternative that enables dynamic statistical profiling of an application's communication activity using message sampling. We have implemented an operational prototype, named PHOTON, and our evidence shows that this new approach can provide an accurate, low-overhead, tractable alternative for performance analysis of communication activity. PHOTON consists of two components: a Message Passing Interface (MPI) profiling layer that implements sampling and analysis, and a modified MPI runtime that appends a small but necessary amount of information to individual messages. More importantly, this alternative enables an assortment of runtime analysis techniques so that, in contrast to post-mortem, trace-based techniques, the raw performance data can be jettisoned immediately after analysis. Our investigation shows that message sampling can reduce overhead to imperceptible levels for many applications. Experiments on several applications demonstrate the viability of this approach. For example, with one application, our technique reduced the analysis overhead from 154% for traditional tracing to 6% for statistical profiling. We also evaluate different sampling techniques in this framework. The coverage of the sample space provided by purely random sampling is superior to counter- and timer-based sampling. Also, PHOTON's design reveals that frugal modifications to the MPI runtime system could facilitate such techniques on production computing systems, and it suggests that this sampling technique could execute continuously for long-running applications.