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The Spy Element Method - A universal approach to complex computing on manycore processors

机译:间谍元素方法-在许多核心处理器上进行复杂计算的通用方法

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This work presents a general approach, coined "Spy Element Method" (SEM), for parallelising workloads to run on manycore processors and featuring dynamic dependencies between different data items, such as graph traversals, remeshing methods and particle simulations. In the SEM, appropriately defined entities, denominated "spy elements", inspect their neighbourhood and without atomic memory operations update in parallel all the dynamic data dependencies, including those between themselves, between the original objects of the current problem and among entities of different kind. The application of the SEM to meshless simulation models obviates the use of binning algorithms relying on sorting or atomics, and concomitantly renders their implementation fully particle centric. On the NVIDIA GeForce GTX 480, an optimised particle-based fluid simulator runs 1.4-2.3 times faster when the binning aided by the state-of-the-art parallel sorter is eliminated and data updating is delegated to the SEM, and is at least 40 times faster than a highly optimised single-threaded version running on a single core of the Intel Xeon X5650 2.66 GHz.
机译:这项工作提出了一种通用方法,称为“间谍元素方法”(SEM),用于并行化工作负载以在许多核心处理器上运行,并具有不同数据项之间的动态相关性,例如图形遍历,重新网格化方法和粒子模拟。在SEM中,适当定义的实体(称为“间谍元素”)检查它们的邻域,并且在没有原子存储操作的情况下并行更新所有动态数据依赖关系,包括它们之间,当前问题的原始对象之间以及不同种类的实体之间的依赖关系。 。 SEM在无网格仿真模型中的应用避免了依赖于分类或原子的分级算法的使用,并因此使它们的实现完全以粒子为中心。在NVIDIA GeForce GTX 480上,优化的基于粒子的流体模拟器的运行速度提高了1.4-2.3倍,这得益于消除了最新并行分类器的分箱并将数据更新委托给SEM的情况,至少比在Intel Xeon X5650 2.66 GHz单核上运行的高度优化的单线程版本快40倍。

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