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Barcelona Supercomputing Center

Barcelona Supercomputing Center Barcelona Supercomputing Center 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%

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Barcelona Supercomputing Center

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  1. Barcelona Supercomputing Center

  2. Barcelona Supercomputing Center • 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 Life Sciences Earth Sciences Engineering apps Users • 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 • Performance analysis • Tracing: scalable/online, sampling • Visualization: Paraver • Automatic analysis: spectral, clustering,… • Methodologies and training material • Integration with other tools • Resource management • OS scheduling: resource/power aware job scheduling, dynamic load balancing • Scalable file systems • Efficient execution on distributed computing environments: GRIDSs @ MN/RES, Grid I/O, heterogenous workloads • Management for next-generation data centers: virtualization • 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

  4. 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 #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); CellSs GridSs StarSs SMPSs CompSs (Java) GPUSs ClusterSs ClearSpeedSs for (i=0; i<N; i+=BS) // C=A+B vadd3 ( &A[i], &B[i], &C[i]); ... for (i=0; i<N; i+=BS) // sum(C[i]) accum (&C[i], &sum); ... for (i=0; i<N; i+=BS) // B=sum*A scale_add (sum, &E[i], &B[i]); ... for (i=0; i<N; i+=BS) // A=C+D vadd3 (&C[i], &D[i], &A[i]); ... for (i=0; i<N; i+=BS) // E=C+F vadd3 (&C[i], &F[i], &E[i]); Programming models

  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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