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Libraries, Languages, and Execution Models for Terascale Applications William D. Gropp Argonne National Laboratory. Center for Programming Models for Scalable Parallel Computing: Project Meeting Report. Participants. Coordinating Principal Investigator:

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center for programming models for scalable parallel computing project meeting report

Libraries, Languages, and Execution Modelsfor Terascale

William D.

Argonne National Laboratory

Center for Programming Models for Scalable Parallel Computing:Project Meeting Report


Coordinating Principal Investigator:

  • Ewing Lusk – Argonne National Laboratory

Co-Principal Investigators (Laboratories):

  • William Gropp – Argonne National Laboratory
  • Ricky Kendall – Ames Laboratory
  • Jarek Nieplocha – Pacific Northwest National Laboratory

Co-Principal Investigators (Universities):

  • Barbara Chapman – University of Houston
  • Guang Gao – University of Delaware
  • John Mellor-Crummey – Rice University
  • Robert Numrich – University of Minnesota
  • Dhabaleswar Panda – Ohio State University
  • Thomas Sterling – California Institute of Technology
  • Marianne Winslett – University of Illinois
  • Katherine Yelick – University of California, Berkeley
problem statement
Problem Statement
  • Problem: Current programming models have enabled development of scalable applications on current large-scale computers, but the application development process itself remains complex, lengthy, and expensive, obstructing progress in scientific application development.
  • Solution: Facilitate application development by providing standard libraries, convenient parallel programming languages, and petaflops-targeted advanced programming models.
  • Goals: An array of attractive options for convenient, efficient, development of scalable, efficient scientific applications for terascale computers
a three pronged approach to next generation programming models
A Three-Pronged Approach to Next-Generation Programming Models
  • Extensions to existing library-based models
    • MPI (-2; extensions)
    • Global Arrays and extensions
    • Portable SHMEM
  • Robust implementations of language-based models
    • UPC
    • Co-Array Fortran
    • Titanium
    • OpenMP optimizations
  • Advanced models for advanced architectures
    • Multithreaded, PIM-based machines, Gilgamesh, etc.
relationships among the parts

Application Programming Models

Message Passing

Remote Memory

Shared Memory

Mixed Models

Language Extensions

New Models

Model Instances






OpenMP + MPI





Implementation Substrate

Common Runtime



Panda Parallel I/O

CAF Packages/ Modules

Open64 Compiler


Communication Firmware

MPP Switches




Relationships Among the Parts
  • Libraries for the remote memory access model
    • MPI and MPI-2
    • Global Arrays
      • GA combine higher-level model with efficiency for application convenience
    • GP-SHMEM
      • Popular Cray T3E model made portable
    • Co-Array Fortran library
      • Object-based scientific library, written in CAF
  • Three languages providing a software global address space (suitable for distributed memory) and parallelism
    • CAF (Co-Array Fortran)
    • UPC (Unified Parallel C)
    • Titanium (parallel Java)
  • One language for shared memory
    • Scalable OpenMP
  • The Open64 compiler infrastructure
    • Industrial strength compiler for C, Fortran 9x, C++
    • Used in the above projects
    • One contribution to the community
cross project infrastructure
Cross-Project Infrastructure
  • Runtime communication approaches
    • Exploiting NICs in support of parallel programming models
    • ARMCI
    • GASNet
  • I/O
    • Active buffering in Panda
    • MPI-IO and parallel file systems
      • Integrating active buffering into ROMIO implementation of MPI-IO
    • Scalable I/O for parallel languages
      • UPC
      • CAF I/O
new programming models
New Programming Models
  • Defining a new execution model
    • Semantics first
      • Define for performance
        • Must provide the enormous benefit Bill Camp mentioned
      • Define to support best algorithms in support of applications
      • Define for likely HPC hardware, including
        • Many (zillions) processors
        • Deep memory hierarchy
        • Some hardware support for programming model
      • Likely to have some kind of precisely relaxed memory consistency model
        • Common feature of all of the high performance libraries and languages in the project (even OpenMP)
    • Experiments with new concepts such as percolation (move program to data instead of data to program)
connections with other programs
Connections With Other Programs
  • Applications from SciDAC, NSF/PACI, etc.
  • DARPA HPCS Program
    • John Mellor-Crummey (Rice) for HP
    • Bob Numrich (UMN) for SGI
    • Thomas Sterling (JPL/Caltech) for Cray
    • Kathy Yelick (Berkeley) for SUN
    • Guang Gao (U Delaware) IBM
    • ANL a member of Cray Affiliates program
  • Open64 Community
  • OpenMP (U Houston formed a company to join ARB, since only companies can be members )
  • IBM Blue Gene/L and QCDoC
  • More…