Insightful Performance Analysis of Many-Task Runtimes through Tool-Runtime Integration

Date and time: 
Friday, June 2, 2017 - 15:30
220 Deschutes
Nicholas Chaimov
University of Oregon
  • Allen Malony (Chair)
  • Boyana Norris
  • Hank Childs
  • Gregory Bothun/Physics


Future supercomputers will require application developers to expose much more parallelism than current applications expose. In order to assist application developers in structuring their applications such that this is possible, new programming models and libraries are emerging, theĀ many-task runtimes, to allow for the expression of orders of magnitude more parallelism than currently existing models.

This dissertation describes the challenges that these emerging many-task runtimes will place on performance analysis, and proposes deep integration between runtimes and performance tools as a means of producing correct, insightful, and actionable performance results. I show how tool-runtime integration can be used to aid programmer understanding of performance characteristics and to provide online performance feedback to the runtime for Unified Parallel C (UPC), High Performance ParalleX (HPX), Apache Spark, the Open Community Runtime, and the OpenMP runtime.