High-level management of communication schedules in HPF-like languages
ICS '98 Proceedings of the 12th international conference on Supercomputing
Performance of hybrid message-passing and shared-memory parallelism for discrete element modeling
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
MPI versus MPI+OpenMP on IBM SP for the NAS benchmarks
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Extending OpenMP for NUMA machines
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Coastal ocean modeling of the U.S. west coast with multiblock grid and dual-level parallelism
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Terascale spectral element dynamical core for atmospheric general circulation models
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
IPPS '99/SPDP '99 Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing
VFC: The Vienna Fortran Compiler
Scientific Programming
Performance Modeling of Communication and Computation in Hybrid MPI and OpenMP Applications
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 2
A framework for an automatic hybrid MPI+OpenMP code generation
Proceedings of the 19th High Performance Computing Symposia
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Clusters of SMPs are hybrid-parallel architectures that combine the main concepts of distributed-memory and shared-memory parallel machines. Although SMP clusters are widely used in the high performance computing community, there exists no single programming paradigm that allows exploiting the hierarchical structure of these machines. Most parallel applications deployed on SMP clusters are based on MPI, the standard API for distributed-memory parallel programming, and thus may miss a number of optimization opportunities offered by the shared memory available within SMP nodes. In this paper we present extensions to the data parallel programming language HPF and associated compilation techniques for optimizing HPF programs on clusters of SMPs. The proposed extensions enable programmers to control key aspects of distributed-memory and shared-memory parallelization at a high-level of abstraction. Based on these language extensions, a compiler can adopt a hybrid parallelization strategy which closely reflects the hierarchical structure of SMP clusters by automatically exploiting shared-memory parallelism based on OpenMP within cluster nodes and distributed-memory parallelism utilizing MPI across nodes. We describe the implementation of these features in the VFC compiler and present experimental results which show the effectiveness of these techniques.