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: GRIDSs @ 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: GRIDSs @ 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<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]);

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