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, the 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.

This dissertation includes previously published co-authored material.