1 / 5

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%

rocio
Download Presentation

Barcelona Supercomputing Center

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related