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A first multi-GPU/multi-node implementation of the open computing abstraction layer

机译:开放计算抽象层的第一个多GPU /多节点实现

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Here we present a first multi-node/multi-GPU implementation of OpenCAL for grid-based high-performance numerical simulation. OpenCL and MPI have been adopted as low-level APIs for maximum portability and performance evaluated with respect to three different benchmarks, namely a Sobel edge detection filter, a Julia fractal generator, and the SciddicaT Cellular Automata model for fluid-flows simulation. Different hardware configurations of a dual-node test cluster have been considered, allowing for executions up to four GPUs. Optimal performance has been achieved in consideration of the compute/memory bound nature of both benchmarks and hardware configurations. (C) 2018 Elsevier B.V. All rights reserved.
机译:在这里,我们介绍了OpenCAL的第一个多节点/多GPU实现,用于基于网格的高性能数值模拟。 OpenCL和MPI已被用作低级API,以针对三个不同的基准(即Sobel边缘检测过滤器,Julia分形生成器和用于流体流动模拟的SciddicaT细胞自动机模型)评估了最大的可移植性和性能。已经考虑了双节点测试集群的不同硬件配置,最多可以执行四个GPU。考虑到基准测试和硬件配置的计算/内存绑定性质,已实现了最佳性能。 (C)2018 Elsevier B.V.保留所有权利。

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