MRNet: A Software-Based Multicast/Reduction Network for Scalable Tools
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
The Tau Parallel Performance System
International Journal of High Performance Computing Applications
The Scalasca performance toolset architecture
Concurrency and Computation: Practice & Experience - Scalable Tools for High-End Computing
Scaling performance tool MPI communicator management
EuroMPI'11 Proceedings of the 18th European MPI Users' Group conference on Recent advances in the message passing interface
Review: Energy-aware performance analysis methodologies for HPC architectures-An exploratory study
Journal of Network and Computer Applications
Comprehensive job level resource usage measurement and analysis for XSEDE HPC systems
Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery
Enabling comprehensive data-driven system management for large computational facilities
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
On the usefulness of object tracking techniques in performance analysis
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Scalasca is an open-source toolset that can be used to analyze the performance behavior of parallel applications and to identify opportunities for optimization. Target applications include simulation codes from science and engineering based on the parallel programming interfaces MPI and/or OpenMP. Scalasca, which has been specifically designed for use on large-scale machines such as IBM Blue Gene and Cray XT, integrates runtime summaries suitable to obtain a performance overview with in-depth studies of concurrent behavior via event tracing. Although Scalasca was already successfully used with codes running with 294,912 cores on a 72-rack Blue Gene/P system, the current software design shows scalability limitations that adversely affect user experience and that will present a serious obstacle on the way to mastering larger scales in the future. In this paper, we outline how to address the two most important ones, namely the unification of local identifiers at measurement finalization as well as collating and displaying analysis reports.