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Avoid Data Race
The Experts below are selected from a list of 1779 Experts worldwide ranked by ideXlab platform
Issei Fujishiro – One of the best experts on this subject based on the ideXlab platform.
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IWOMP – Optimization Strategies Using Hybrid MPI+OpenMP Parallelization for Large-Scale Data Visualization on Earth Simulator
Lecture Notes in Computer Science, 2007Co-Authors: Li Chen, Issei FujishiroAbstract:An efficient parallel visualization library has been developed for the Earth Simulator. Due to its SMP cluster architecture, a three-level hybrid parallel programming model, including message passing for inter-SMP node communication, loop directives by OpenMP for intra-SMP node parallelization and vectorization for each processing element (PE) was adopted. In order to get good speedup performance with OpenMP parallelization, many strategies are used in implementing the visualization modules such as thread parallelization by OpenMP considering seed point distributions and flow features for parallel streamline generation, multi-coloring reordering to Avoid Data Race of shared variables, some kinds of coherence removal, and hybrid image-space and object-space parallel for volume rendering. Experimental results on the Earth Simulator demonstrate the feasibility and effectiveness of our methods.
G.V. Lo – One of the best experts on this subject based on the ideXlab platform.
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An SPMD-Like Algorithm for Parallelizing Molecular Dynamics Using OpenMP
Computing in Science & Engineering, 2013Co-Authors: Mingze Bai, Shixin Sun, Hong Tang, Yusheng Dou, G.V. LoAbstract:The efficiency and scalability of early efforts to parallelize molecular dynamics calculations on shared-memory systems using OpenMP have been limited by attempts to Avoid Data Race. Recent work has produced better performance, but involves significant revisions to the serial code. A new algorithm addresses these limitations.
Li Chen – One of the best experts on this subject based on the ideXlab platform.
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IWOMP – Optimization Strategies Using Hybrid MPI+OpenMP Parallelization for Large-Scale Data Visualization on Earth Simulator
Lecture Notes in Computer Science, 2007Co-Authors: Li Chen, Issei FujishiroAbstract:An efficient parallel visualization library has been developed for the Earth Simulator. Due to its SMP cluster architecture, a three-level hybrid parallel programming model, including message passing for inter-SMP node communication, loop directives by OpenMP for intra-SMP node parallelization and vectorization for each processing element (PE) was adopted. In order to get good speedup performance with OpenMP parallelization, many strategies are used in implementing the visualization modules such as thread parallelization by OpenMP considering seed point distributions and flow features for parallel streamline generation, multi-coloring reordering to Avoid Data Race of shared variables, some kinds of coherence removal, and hybrid image-space and object-space parallel for volume rendering. Experimental results on the Earth Simulator demonstrate the feasibility and effectiveness of our methods.