Lawrence Berkeley National Laboratory

This work evaluates performance variability in the Cray Aries dragonfly network and characterizes its impact on MPI Allreduce. The execution time of Allreduce is limited by the performance of the slowest participating process, which can vary by more than an order of magnitude. We utilize counters from the network routers to provide a better understanding of how competing workloads can influence performance. Specifically, we examine the relationships between message size, process counts, Aries counters and the Allreduce communication-Time. Our results suggest that competing traffic from other jobs can significantly impact performance on the Aries Dragonfly Network. Furthermore, we show that Aries network counters are a valuable tool, explaining up to 70% of the performance variability for our experiments on a large-scale production system.