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Barcelona Supercomputing Center. The BSC-CNS objectives: R&D in Computer Sciences, Life Sciences and Earth Sciences. Supercomputing support to external.

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Presentation on theme: "Barcelona Supercomputing Center. The BSC-CNS objectives: R&D in Computer Sciences, Life Sciences and Earth Sciences. Supercomputing support to external."— Presentation transcript:

1 Barcelona Supercomputing Center

2 The BSC-CNS objectives: R&D in Computer Sciences, Life Sciences and Earth Sciences. Supercomputing support to external research. BSC-CNS is a consortium that includes : the Spanish Government (MEC) – 51% the Catalonian Government (DIUE) – 37% the Technical University of Catalonia (UPC) – 12% 300 people

3 Research areas Influence the way machines are built, programmed and used Through demonstration, ideas, cooperation with manufacturers & products e-science Programming models Evolving standarts (OpenMP x.y) Prototyping infrastructure (mercurium, nanos library, …) Dependeces/data-flow (StarSs for Cell, SMP, GPU, Grid) Hierarchical/hybrid (MPI/SMPSs, NestedSs, …) Software Distributed Shared Memory Use of Transactional memory Programming models Evolving standarts (OpenMP x.y) Prototyping infrastructure (mercurium, nanos library, …) Dependeces/data-flow (StarSs for Cell, SMP, GPU, Grid) Hierarchical/hybrid (MPI/SMPSs, NestedSs, …) Software Distributed Shared Memory Use of Transactional memory Resource management OS scheduling: resource/power aware job scheduling, dynamic load balancing Scalable file systems Efficient execution on distributed computing environments: MN/RES, Grid I/O, heterogenous workloads Management for next-generation data centers: virtualization Resource management OS scheduling: resource/power aware job scheduling, dynamic load balancing Scalable file systems Efficient execution on distributed computing environments: MN/RES, Grid I/O, heterogenous workloads Management for next-generation data centers: virtualization Performance analysis Tracing: scalable/online, sampling Visualization: Paraver Automatic analysis: spectral, clustering,… Methodologies and training material Integration with other tools Performance analysis Tracing: scalable/online, sampling Visualization: Paraver Automatic analysis: spectral, clustering,… Methodologies and training material Integration with other tools Prediction and evaluation infrastructure Dimemas: multiscale simulation Interconnection network: overlap, contention, … Node and microarchitecture level simulators: MPsim, TaskSim Architecture support for programming models and runtimes Prediction and evaluation infrastructure Dimemas: multiscale simulation Interconnection network: overlap, contention, … Node and microarchitecture level simulators: MPsim, TaskSim Architecture support for programming models and runtimes Users Earth Sciences Life Sciences Engineering apps

4 Programming models Implementations on top of other low level run times, FPGAs, OpenCL Granularity control Locality aware scheduling Application porting Hybrid MPI/StarSs and comparison with other models Load balancing in nested/hybrid implementations Instrumentation and analysiss for task based systems StarSs CellSs SMPSs GPUSs GridSs ClearSpeedSs ClusterSs CompSs (Java) #pragma css task input(A, B) output(C) void vadd3 (float A[BS], float B[BS], float C[BS]); #pragma css task input(sum, A) output(B) void scale_add (float sum, float A[BS], float B[BS]); #pragma css task input(A) inout(sum) void accum (float A[BS], float *sum); for (i=0; i

5 Performance tools Analysis of applications at large scale Maximize ratio of captured information / emitted data Intelligent on line data reduction Mixed instrumentation and sampling Advanced modeling/prediction of sequential computation behavior Memory behavior Use classification techniques of hardware counter metrics to identify potentially interesting transformations CPI STACK model for sequential computation parts


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