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Published byHailey Duffy Modified over 11 years ago
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Performance Analysis Tools for High-Performance Computing Daniel Becker 25-03-2010
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German Research School for Simulation Sciences Joint venture between Forschungszentrum Jülich and RWTH Aachen University –Research and education in the simulation sciences –International Masters program –Ph.D. program
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Jülich Supercomputing Centre Research in Computational Science Computer Science Mathematics Jülich BG/P 294,912 cores Jülich Nehalem Cluster 26,304 cores
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Scalable performance-analysis toolset for parallel codes Integrated performance analysis process –Performance overview on call-path level via runtime summarization –In-depth study of application behavior via event tracing –Switching between both options without recompilation or relinking Supported programming models –MPI-1, MPI-2 one-sided communication –OpenMP (basic features) Available under New BSD –http://www.scalasca.org/
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Research Challenges Scalability –Collection and representation of necessary runtime information –Analysis and visualization of performance behavior Analysis of asynchronous tasks –Examples: OpenMP 3.0, StarSs, CUDA, OpenCL,... –Threads and tasks - different dimensions of parallelism –Identification of performance properties –Representation of asynchronous performance data with respect to call-path profile data –Measurement, analysis and result presentation –Integration with current analysis approaches
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