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Comprehending the performance bottlenecks at the core of the intricatehardware-software interactions exhibited by highly parallel programs on HPCclusters is crucial. This paper sheds light on the issue of automaticallyasynchronous MPI communication in memory-bound parallel programs on multicoreclusters and how it can be facilitated. For instance, slowing down MPIprocesses by deliberate injection of delays can improve performance if certainconditions are met. This leads to the counter-intuitive conclusion that noise,independent of its source, is not always detrimental but can be leveraged forperformance improvements. We employ phase-space graphs as a new tool tovisualize parallel program dynamics. They are useful in spotting certainpatterns in parallel execution that will easily go unnoticed with traditionaltracing tools. We investigate five different microbenchmarks and applicationson different supercomputer platforms: an MPI-augmented STREAM Triad, twoimplementations of Lattice-Boltzmann fluid solvers, and the LULESH and HPCGproxy applications.

Making Applications Faster by Asynchronous Execution: Slowing Down Processes or Relaxing MPI Collectives