This paper gives an overview about the Score-P performance measurement infrastructure which is being jointly developed by leading HPC performance tools groups. It motivates the advantages of the joint undertaking from both the developer and the user perspectives, and presents the design and components of the newly developed Score-P performance measurement infrastructure. Furthermore, it contains first evaluation results in comparison with existing performance tools and presents an outlook to the long-term cooperative development of the new system.
Abstract.Version 3.0 of the OpenMP specification introduced the task construct for the explicit expression of dynamic task parallelism. Although automated load-balancing capabilities make it an attractive parallelization approach for programmers, the difficulty of integrating this new dimension of parallelism into traditional models of performance data has so far prevented the emergence of appropriate performance tools. Based on our earlier work, where we have introduced instrumentation for task-based programs, we present initial concepts for analyzing the data delivered by this instrumentation. We define three typical performance problems related to tasking and show how they can be visually explored using event traces. Special emphasis is placed on the event model used to capture the execution of task instances and on how the time consumed by the program is mapped onto tasks in the most meaningful way. We illustrate our approach with practical examples.
With the task construct, the OpenMP 3.0 specification introduces an additional level of parallelism that challenges established schemes of performance profiling. First, a thread may execute a sequence of interleaved task fragments the profiling system must properly distinguish to enable correct performance analyses. Furthermore, the additional parallelization dimension requires new visualization methods for presenting analysis results. Finally, as a new programming paradigm, tasking implicitly introduces paradigm-specific performance issues and creates a need for corresponding optimization strategies. This paper presents solutions to overcome the challenges of profiling applications based on OpenMP tasks. Second, the paper describes metrics that may help uncover performance problems related to tasking. We present an implementation of our solution within the Score-P performance measurement system, which we evaluate using the Barcelona OpenMP Task Suite.
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