HPCwire: OpenMP on Clusters

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May 19, 2006

OpenMP on Clusters

by Jay Hoeflinger and Larry Meadows, Intel Corporation

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Abstract

OpenMP* was designed to unify the directive languages of shared memory multiprocessors across the industry to make it easier to write portable parallel programs. OpenMP represents a high-level language of parallelism compared to programming with Posix threads or Windows threads. It also represents a much easier programming model than does MPI. This effort has been successful and OpenMP has gained many users around the world. Yet, thus far OpenMP has only been useful for programming systems with hardware shared memories. Now, Intel is offering Cluster OpenMP*, which extends the OpenMP programming model to clusters. This article describes the extension to OpenMP that makes this possible, how the system manages to simulate a shared memory across a cluster, how the user ports an OpenMP program to Cluster OpenMP, and ends with a discussion of the amount of effort required to port a code.

Introduction

In the 1980s and 90s, multiprocessor computer manufacturers tried to address the difficulties of programming their computers by supplying directives that could be placed in serial programs that would instruct the compiler to produce parallel code. Each manufacturer produced its own unique set of directives. As programmers moved their programs from machine to machine, they found that they had to recode the directives. To remedy this situation, major players in the industry formed a working group in the mid-90s to unify the directives. The result was the 1997 OpenMP specification for Fortran and the 1998 specification for C/C++. This effort has been successful, and the OpenMP directive language has been adopted across the industry.

The OpenMP paradigm for parallel programming differs significantly from the earlier message passing solutions (such as PVM and MPI), and from explicit threading (Posix* threads or Windows* threads). The first difference that people notice is that OpenMP consists mostly of directives, whereas PVM, MPI and the threading methods consist solely of library routines. The effect of this is that OpenMP's effects can be switched off by a compiler switch, removing the OpenMP parallelism, whereas programs using library-based parallelism are permanently changed into parallel programs.

Another obvious difference versus message passing is that data movement for message passing programs must be explicitly programmed by the programmer, while data movement in OpenMP programs happens automatically when threads read and write variables. This means that in addition to the code for the problem being solved, the message passing programmer must write a program layer to move the data between processors.

Both of these differences translate directly into lower programming costs and lower maintenance costs for OpenMP programs. With OpenMP, you program "what" to do, while with message passing and explicit threading you program "how" to do it.

In this sense, OpenMP could be said to be a high-level language of parallel programming, while explicit threading and message-passing programming is more akin to an "assembly language" of parallel programming.

A drawback to OpenMP is that it requires a shared memory, so up-to-now this has limited its use to a single multiprocessor machine. Now, Intel's Cluster OpenMP removes that limitation. Cluster OpenMP makes it possible to run an OpenMP program across a cluster of multiprocessors. The shared memory is simulated by a software layer implementing a distributed shared memory (DSM).

Cluster OpenMP Extension to OpenMP

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