Scalable communication event tracing via clustering

Abstract

Communication traces help developers of high-performance computing (HPC) applications understand and improve their codes. When run on large-scale HPC facilities, the scalability of tracing tools becomes a challenge. To address this problem, traces can be clustered into groups of processes that exhibit similar behavior. Instead of collecting trace information of each individual node, it then suffices to collect a trace of a small set of representative nodes, namely one per cluster. However, clustering algorithms themselves need to have low overhead, be scalable, and adapt to application characteristics. We devised an adaptive clustering algorithm for large-scale applications called ACURDION that traces the MPI communication of code with O(log P) time complexity. First, ACURDION identifies the parameters that differ across processes by using a logarithmic algorithm called Adaptive Signature Building. Second, it clusters the processes based on those parameters. Experiments show that collecting traces of just nine nodes/clusters suffices to capture the communication behavior of all nodes for a wide set of HPC benchmarks codes while retaining sufficient accuracy of trace events and parameters. In summary, ACURDION improves trace scalability and automation over prior approaches.