ScaLAPACK user's guide
X10: an object-oriented approach to non-uniform cluster computing
OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Sequoia: programming the memory hierarchy
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Sequoia: programming the memory hierarchy
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Compilation for explicitly managed memory hierarchies
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
Parallel Programmability and the Chapel Language
International Journal of High Performance Computing Applications
Handling task dependencies under strided and aliased references
Proceedings of the 24th ACM International Conference on Supercomputing
Programming the memory hierarchy revisited: supporting irregular parallelism in sequoia
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
Mint: realizing CUDA performance in 3D stencil methods with annotated C
Proceedings of the international conference on Supercomputing
Productive cluster programming with OmpSs
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Productive Programming of GPU Clusters with OmpSs
IPDPS '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium
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The need for features for managing complex data accesses in modern programming models has increased due to the emerging hardware architectures. HPC hardware has moved towards clusters of accelerators and/or multicores, architectures with a complex memory hierarchy exposed to the programmer. We present the implementation of data regions on the OmpSs programming model, a high-productivity annotation-based programming model derived from OpenMP. This enables the programmer to specify regions of strided and/or overlapped data used by the parallel tasks of the application. The data will be automatically managed by the underlying run-time environment, which could transparently apply optimization techniques to improve performance. This approach based on a high-productivity programming model contrasts with more direct approaches like MPI, where the programmer has to explicitly deal with the data management. It is generally believed that these are capable of achieving the best possible performance, so we also compare the performance of several OmpSs applications against well-known counterparts MPI implementations obtaining comparable or better results.