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Parallel LU factorization of sparse matrices on FPGA-based configurable computing engines

机译:基于FPGA的可配置计算引擎上稀疏矩阵的并行LU分解

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Configurable computing, where hardware resources are configured appropriately to match specific hardware designs, has recently demonstrated its ability to significantly improve performance for a wide range of computation-intensive applications. With steady advances in silicon technology, as predicted by Moore's Law, Field-Programmable Gate Array (FPGA) technologies have enabled the implementation of System-on-a-Programmable-Chip (SOPC or SOC) computing platforms, which, in turn, have given a significant boost to the field of configurable computing. It is possible to implement various specialized parallel machines in a single silicon chip. In this paper, we describe our design and implementation of a parallel machine on an SOPC development board, using multiple instances of a soft IP configurable processor; we use this machine for LU factorization. LU factorization is widely used in engineering and science to solve efficiently large systems of linear equations. Our implementation facilitates the efficient solution of linear equations at a cost much lower than that of supercomputers and networks of workstations. The intricacies of our FPGA-based design are presented along with tradeoff choices made for the purpose of illustration. Performance results prove the viability of our approach.
机译:可配置计算,其中硬件资源被适当配置以匹配特定的硬件设计,最近已经证明了它能够显着提高各种计算密集型应用程序的性能的能力。正如摩尔定律所预言的那样,随着硅技术的稳步发展,现场可编程门阵列(FPGA)技术已实现了可编程芯片系统(SOPC或SOC)计算平台的实现,而该平台又具有极大地促进了可配置计算领域的发展。可以在单个硅芯片中实现各种专用并行机。在本文中,我们将使用软IP可配置处理器的多个实例描述我们在SOPC开发板上并行计算机的设计和实现。我们使用这台机器进行LU分解。 LU分解在工程和科学中广泛用于有效地求解大型线性方程组。我们的实现以比超级计算机和工作站网络低得多的成本促进了线性方程的高效求解。给出了我们基于FPGA的设计的复杂性以及为进行说明而做出的权衡选择。性能结果证明了我们方法的可行性。

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