GPU-accelerated Path-based Timing Analysis
GPU-accelerated Path-based Timing Analysis
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DOI：
10.1109/dac18074.2021.9586316
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Guannan Guo;Tsung-Wei Huang;Yibo Lin;Martin D. F. Wong
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文献类型：
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作者：
Guannan Guo;Tsung-Wei Huang;Yibo Lin;Martin D. F. Wong
基于路径的分析（PBA）是降低松弛悲观主义的设计闭合流中的重要步骤。但是，PBA非常耗时。近年来，有许多平行的PBA算法，但其中大多数在架构上受到CPU并行性的限制，并且不超过几个线程。为了克服这一挑战，我们在本文中提出了一种新的快速准确的PBA算法，通过利用图形处理单元（GPU）的功能。我们介绍了GPU有效的数据结构，高性能内核和有效的CPU-GPU任务分解策略，以加速PBA到一个新的性能里程碑。实验结果表明，我们的方法可以在160万个大门的设计中加快最新算法的速度$ 543 \ times $。在极端情况下，我们的1个CPU和1 GPU的方法优于40 CPU的最新算法，$ 25-45 \ times $。
Path-based Analysis (PBA) is an important step in the design closure flow for reducing slack pessimism. However, PBA is extremely time-consuming. Recent years have seen many parallel PBA algorithms, but most of them are architecturally constrained by the CPU parallelism and do not scale beyond a few threads. To overcome this challenge, we propose in this paper a new fast and accurate PBA algorithm by harnessing the power of graphics processing unit (GPU). We introduce GPU-efficient data structures, high-performance kernels, and efficient CPU-GPU task decomposition strateiges, to accelerate PBA to a new performance milestone. Experimental results show that our method can speed up the state-of-the-art algorithm by $543\times$ on a design of 1.6 million gates with exact accuracy. At the extreme, our method of 1 CPU and 1 GPU outperforms the state-of-the-art algorithm of 40 CPUs by $25-45\times$.