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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
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
research areas
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
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

#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
performance tools
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