Online Critical Path Profiling for Parallel Applications

Abstract

Online monitoring of parallel applications is increasingly important for techniques such as load balancing, protocol adaptation, and online anomaly detection. Unfortunately, existing online monitoring techniques only monitor individual hosts in a distributed-memory parallel application. In this paper, we show how a new monitoring technique, message-centric monitoring, can be used for online monitoring of the complete critical path in distributed-memory parallel applications. Results from an MPI-based message-centric monitoring prototype called IMPuLSE show that it has less than 3% runtime overhead, accurately measures whole-system performance as the application runs, and captures data that can be used by nodes to detect unusual system behaviors at runtime