Parallel multi-level analytical global placement on graphics processing units

J. Cong, Yi Zou
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引用次数: 32

Abstract

GPU platforms are becoming increasingly attractive for implementing accelerators because they feature a larger number of cores with improved programmability. In this paper, we describe our implementation of a state-of-the-art academic multi-level analytical placer mPL on Nvidia's massively parallel GT200 series platforms. We detail our efforts on performance tuning and optimizations. When compared to software implementation on Intel's recent generation Xeon CPU, the speed of the global placement part of mPL is 15× faster on average using a Tesla C1060 card, with comparable WL. (less than 1% WL degradation on average).
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在图形处理单元上并行多级分析全局放置
GPU平台对于实现加速器越来越有吸引力,因为它们具有更多的内核和改进的可编程性。在本文中,我们描述了我们在Nvidia的大规模并行GT200系列平台上实现的最先进的学术多级分析砂矿mPL。我们详细介绍了我们在性能调优和优化方面所做的努力。与英特尔最新一代至强CPU上的软件实现相比,使用特斯拉C1060卡时,mPL的全局放置部分的速度平均快15倍,并具有相当的WL。(平均WL衰减小于1%)。
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