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Parallelizing FPGA Technology Mapping Using Graphics Processing Units (GPUs)

机译:使用图形处理单元(GPU)的并行FPGA技术映射

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GPUs are becoming an increasingly attractive option for obtaining performance speedups for data-parallel applications. FPGA technology mapping is an algorithm that is heavily data parallel; however, it has many features that make it unattractive to implement on a GPU. The algorithm uses data in irregular ways since it is a graph-based algorithm. In addition, it makes heavy use of constructs like recursion which is not supported by GPU hardware. In this paper, we take a state-of-the-art FPGA technology mapping algorithm within Berkeleyȁ9;s ABC package and attempt to parallelize it on a GPU. We show that runtime gains of 3.1x are achievable while maintaining identical quality as demonstrated by running these netlists through Alteraȁ9;s Quartus II place-and-route tool.
机译:GPU成为获取数据并行应用程序性能提升的一种越来越有吸引力的选择。 FPGA技术映射是一种高度并行数据的算法。但是,它具有许多功能,因此在GPU上实现吸引力不大。该算法是基于图形的算法,因此以不规则的方式使用数据。此外,它大量使用了GPU硬件不支持的递归等构造。在本文中,我们采用Berkeleyȁ9的ABC软件包中的最新FPGA技术映射算法,并尝试在GPU上并行化它。我们证明,通过Altera®9的Quartus II布局布线工具运行这些网表,可以在保持相同质量的同时达到3.1倍的运行时间增益。

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