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Showing 1 to 7 of 7 for “"data redistribution"”.
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An MPI-based 2D data redistribution library for dense arrays
In HPC, data redistributions (reorganizations) are used in parallel applications to improve performance and/or provide data-locality compatibility with sequences of parallel operations. Data reorganization refers to changing the logical and physical arrangement of data (such as dense arrays or …
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High performance virtual architecture parallel libraries with data redistribution for multicomputers
… programs perform computations on distributed data. Transfer of data between processors is carried out using communication operations which are normally costly. Introduction of parallel library systems gives rise to four important issues. The first one is the design of the library routine …
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Simultaneous exploitation of task and data parallelism in regular scientific applications
… simultaneous exploitation of task and data parallelism. Our optimization is implemented as part of the PARADIGM HPF compiler framework and as part of a MATLAB compiler framework we have developed. The intuitive idea behind the optimization is the use of task parallelism to control the …
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Workload Management for Data-Intensive Services
<p>Data-intensive web services are typically composed of three tiers: i) a display tier that interacts with users and serves rich content to them, ii) a storage tier that stores the user-generated or machine-generated data used to create this content, and iii) an analytics tier that runs data …
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Data and Processor Mapping Strategies for Dynamically Resizable Parallel Applications
… in terms of minimizing the cost of data redistribution, choosing and allocating the right set of additional processors, and focusing on the performance of the application after resizing. We explore the tradeoffs between these goals on both homogeneous and heterogeneous clusters. We …
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Improving the performance and scalability of algebraic multigrid solvers through applied performance modeling
… tasks: guiding an algorithm that redistributes data to trade communication for computation, and informing the selection of thread/task mixes when using a hybrid programming model. Our performance models accurately predict the performance of the AMG solve cycle on multiple platforms, capturing …
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ReSHAPE: A Framework for Dynamic Resizing of Parallel Applications
As terascale supercomputers become more common, and as the high-performance computing community turns its attention to petascale machines, the challenge of providing effective resource management for high-end machines grows in both importance and difficulty. These computing resources are by …