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Hybrid Parallelism for Volume Rendering on Large-, Multi-, and Many-Core Systems

机译:在大型,多核和多核系统上进行体积渲染的混合并行性

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With the computing industry trending toward multi- and many-core processors, we study how a standard visualization algorithm, raycasting volume rendering, can benefit from a hybrid parallelism approach. Hybrid parallelism provides the best of both worlds: using distributed-memory parallelism across a large numbers of nodes increases available FLOPs and memory, while exploiting shared-memory parallelism among the cores within each node ensures that each node performs its portion of the larger calculation as efficiently as possible. We demonstrate results from weak and strong scaling studies, at levels of concurrency ranging up to 216,000, and with data sets as large as 12.2 trillion cells. The greatest benefit from hybrid parallelism lies in the communication portion of the algorithm, the dominant cost at higher levels of concurrency. We show that reducing the number of participants with a hybrid approach significantly improves performance.
机译:随着计算行业朝着多核和多核处理器的趋势发展,我们研究了标准的可视化算法(光线投射体积渲染)如何从混合并行方法中受益。混合并行性提供了两全其美的优势:在大量节点上使用分布式内存并行性会增加可用的FLOP和内存,而在每个节点内的内核之间利用共享内存并行性可确保每个节点执行较大计算所需要的部分:尽可能有效。我们展示了弱规模研究和强规模研究的结果,这些研究的并发水平高达216,000,数据集多达12.2万亿个单元。混合并行性的最大好处在于算法的通信部分,这是较高并发级别的主要成本。我们表明,使用混合方法减少参与者的数量可以显着提高绩效。

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