CSC/ECE 506 Spring 2010/ch 2 maf: Difference between revisions

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===Overview===
===Overview===
Whereas the shared memory and message passing models focus on how parallel tasks access common data, the data parallel model focuses on what subsets of the data each parallel task will access.


===Comparing the Data Parallel Model with the Shared Memory and Message Passing Models===
===Comparing the Data Parallel Model with the Shared Memory and Message Passing Models===

Revision as of 18:02, 28 January 2010

Supplement to Chapter 2: The Data Parallel Programming Model

This chapter is a supplement to Chapter 2 of the Solihin textbook. The textbook covers the shared memory and message passing parallel programming models. However, it does not address the data parallel model

Overview

Whereas the shared memory and message passing models focus on how parallel tasks access common data, the data parallel model focuses on what subsets of the data each parallel task will access.

Comparing the Data Parallel Model with the Shared Memory and Message Passing Models

Comparison between shared memory, message passing, and data parallel programming models (adapted from Solihin 2008, page 22).
Aspects Shared Memory Message Passing Data Parallel
Communication implicit (via loads/stores) explicit messages implicit
Synchronization explicit implicit (via messages) typically implicit
Hardware support typically required none
Development effort lower higher higher
Tuning effort higher lower

A Code Example

// Simple sequential code from Solihin 2008, page 25.

for (i = 0; i < 8; i++)
    a[i] = b[i] + c[i];
sum = 0;
for (i = 0; i < 8; i++)
    if (a[i] > 0)
        sum = sum + a[i];
Print sum;
// Data parallel implementation in C++ with OpenMP.

#include <omp.h>
#include <iostream>

int main(void)
{
    double a[8], b[8], c[8], localSum[2];
    long s = 4;
    int id, i;

    #pragma omp parallel for private(id, i) reduction(+:s)
    for (int i = 0; i < 8; i++)
    {
        a[i] = b[i] + c[i];
    }

    for (int i = 0; i < 2; i++) localSum[i] = 0;

    #pragma omp parallel for private(id, i) reduction(+:s)
    for (int i = 0; i < 8; i++)
    {
        id = omp_get_thread_num();
        if (a[i] > 0)
            localSum[id] = localSum[id] + a[i];
    }

    double sum = localSum[0] + localSum[1];
    std::cout << sum << std::endl;
}

Hardware Examples

References

  • David E. Culler, Jaswinder Pal Singh, and Anoop Gupta, Parallel Computer Architecture: A Hardware/Software Approach, Morgan-Kauffman, 1999.
  • Ian Foster, Designing and Building Parallel Programs, Addison-Wesley, 1995.
  • Magne Haveraaen, "Machine and collection abstractions for user-implemented data-parallel programming," Scientific Programming, 8(4):231-246, 2000.
  • W. Daniel Hillis and Guy L. Steele, Jr., "Data parallel algorithms," Communications of the ACM, 29(12):1170-1183, December 1986.
  • Alexander C. Klaiber and Henry M. Levy, "A comparison of message passing and shared memory architectures for data parallel programs," in Proceedings of the 21st Annual International Symposium on Computer Architecture, April 1994, pp. 94-105.
  • Yan Solihin, Fundamentals of Parallel Computer Architecture: Multichip and Multicore Systems, Solihin Books, 2008.