Productive Cluster Programming with OmpSs

• Published in: Euro-Par 2011 Parallel Processing pp. 555 - 566
• Main Authors: Bueno, Javier, Martinell, Luis, Duran, Alejandro, Farreras, Montse, Martorell, Xavier, Badia, Rosa M., Ayguade, Eduard, Labarta, Jesús
• Format: Book Chapter Publication
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2011; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Address Space; Arquitectura de computadors; Arquitectures paral·leles; Distribute Shared Memory; Informàtica; Master Node; Parallel processing (Electronic computers); Processament en paral·lel (Ordinadors); Remote Node; Task Parallelism; Àrees temàtiques de la UPC
• ISBN: 9783642233999; 3642233996; 9783642297403; 3642297404
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-23400-2_52

Clusters of SMPs are ubiquitous. They have been traditionally programmed by using MPI. But, the productivity of MPI programmers is low because of the complexity of expressing parallelism and communication, and the difficulty of debugging. To try to ease the burden on the programmer new programming models have tried to give the illusion of a global shared-address space (e.g., UPC, Co-array Fortran). Unfortunately, these models do not support, increasingly common, irregular forms of parallelism that require asynchronous task parallelism. Other models, such as X10 or Chapel, provide this asynchronous parallelism but the programmer is required to rewrite entirely his application. We present the implementation of OmpSs for clusters, a variant of OpenMP extended to support asynchrony, heterogeneity and data movement for task parallelism. As OpenMP, it is based on decorating an existing serial version with compiler directives that are translated into calls to a runtime system that manages the parallelism extraction and data coherence and movement. Thus, the same program written in OmpSs can run in a regular SMP machine, in clusters of SMPs, or even can be used for debugging with the serial version. The runtime uses the information provided by the programmer to distribute the work across the cluster while optimizes communications using affinity scheduling and caching of data. We have evaluated our proposal with a set of kernels and the OmpSs versions obtain a performance comparable, or even superior, to the one obtained by the same version of MPI.