A Framework for Iterative Stencil Algorithm Synthesis on FPGAs from OpenCL Programming Model (Abstract Only)

Shuo Wang, Yun Liang
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引用次数: 2

Abstract

Iterative stencil algorithms find applications in a wide range of domains. FPGAs have long been adopted for computation acceleration due to its advantages of dedicated hardware design. Hence, FPGAs are a compelling alternative for executing iterative stencil algorithms. However, efficient implementation of iterative stencil algorithms on FPGAs is very challenging due to the data dependencies between iterations and elements in the stencil algorithms, programming hurdle of FPGAs, and large design space. In this paper, we present a comprehensive framework that synthesizes iterative stencil algorithms on FPGAs efficiently. We leverage the OpenCL-to-FPGA tool chain to generate accelerator automatically and perform design space exploration at high level. We propose to bridge the neighboring tiles through pipe and enable data sharing among them to improve computation efficiency. We first propose a homogeneous design with equal tile size. Then, we extend to a heterogeneous design with different tile size to balance the computation among different tiles. Our designs exhibit a large design space in terms of tile structure. We also develop analytical performance models to explore the complex design space. Experiments using a wide range of stencil applications demonstrate that on average our homogeneous and heterogeneous implementations achieve 1.49X and 1.65X performance speedup respectively but with less hardware resource compared to the state-of-the-art.
基于OpenCL编程模型的fpga迭代模板算法综合框架(仅摘要)
迭代模板算法在许多领域都有应用。fpga由于其专用硬件设计的优势,长期以来被用于计算加速。因此,fpga是执行迭代模板算法的一个引人注目的替代方案。然而,由于迭代与模板算法中元素之间的数据依赖关系、fpga的编程障碍以及较大的设计空间,在fpga上有效地实现迭代模板算法是非常具有挑战性的。在本文中,我们提出了一个综合框架,有效地综合了fpga上的迭代模板算法。我们利用OpenCL-to-FPGA工具链自动生成加速器,并在高层次上进行设计空间探索。我们提出通过管道桥接相邻的瓦片,实现瓦片之间的数据共享,以提高计算效率。我们首先提出一个均匀的设计,具有相同的瓷砖大小。然后,我们扩展到不同瓷砖大小的异构设计,以平衡不同瓷砖之间的计算。我们的设计在瓷砖结构上展现了很大的设计空间。我们还开发了分析性能模型来探索复杂的设计空间。使用广泛的模板应用程序的实验表明,平均而言,我们的同构和异构实现分别实现了1.49倍和1.65倍的性能加速,但与最先进的技术相比,硬件资源更少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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