Performance Analysis Tools for High-Performance Computing Daniel Becker 25-03-2010.

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Presentation transcript:

Performance Analysis Tools for High-Performance Computing Daniel Becker

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

Jülich Supercomputing Centre Research in Computational Science Computer Science Mathematics Jülich BG/P 294,912 cores Jülich Nehalem Cluster 26,304 cores

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 –

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