SHF:Small:GPU-Based Many-Core Parallel Simulation of Interconnect and High-Frequency Circuits

SHF:Small：基于 GPU 的互连和高频电路多核并行仿真

• 批准号: 1017090
• 负责人: Sheldon Tan
• 金额: $ 27万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017090&HistoricalAwards=false
• 关键词: SHF; Small; GPU; Based; Many

Parallel computing based on the general purpose Graphic Processing Unit (GPU) provide massive many-core parallelism and can deliver staggering performance improvements over traditional single-core and existing general multi-core computing techniques. The recent introduction of general-purpose GPU (GPGPU) has gained strong interests from the scientific community to tackle many computationally intensive problems. The GPU computing powers, however, have not been fully exploited for many important engineering computing problems in the VLSI design practices. Simulation of massive global interconnects, radio-frequency (RF) and millimeter-wave (MM) integrated circuits (ICs) at very high frequencies remain as difficult problems confronting chip designers. Designing new parallel and scalable computing algorithms, which can unleash the potentials of GPU-based parallel computing techniques, become highly desirable. This research seeks to investigate new parallel simulation approaches to solving massive interconnect circuits and analog/RF/MM integrated circuits based on single node general GPU or networked GPUs on a computer (GPU-cluster). First, the PI will investigate new parallel simulation algorithms based on analytic solution for structured interconnect circuits like on-chip power delivery and clock distribution networks on a GPU or GPU-cluster. Second, the PI proposes developing a very efficient numerical parallel simulation algorithm for analyzing general interconnects. The new algorithm will perform circuit complexity reduction to improve the efficiency. The PI?s team will investigate to parallelize the major computing steps in this method. Third, the PI plans to develop new parallel shooting-Newton methods for high-frequency circuits (RF/MM). The new method will explore structured Krylov-subspace, and GPU-based parallelization to improve efficiency as well as the convergence of RF/MM integrated circuit simulation. The outcome of this research will add significantly to the core knowledge of parallel numerical analysis of linear and nonlinear dynamic systems on the GPU and GPU-cluster systems. By working with the industry partner, the PI expects to bring immediate impacts on the design community to improve the design productivity for nanometer VLSI systems. The research results will also help the electronic design automation (EDA) community to gain more insight in exploring the current and future general-purpose GPUs for parallelizing entire EDA tools on GPUs and multicore systems. The interdisciplinary nature of proposed research and relevant training will allow students to gain critical skills in the highly competitive high-tech job market. This grant will enable the PI to hire more female and underrepresented minority students to further contribute to the diversity in America?s science and technology workforce.

基于通用图形处理单元（GPU）的并行计算提供了大规模的众核并行性，与传统的单核和现有的通用多核计算技术相比，可以带来惊人的性能提升。最近推出的通用 GPU (GPGPU) 引起了科学界的强烈兴趣，以解决许多计算密集型问题。然而，在超大规模集成电路设计实践中，许多重要的工程计算问题还没有充分利用GPU的计算能力。在极高频率下模拟大规模全球互连、射频 (RF) 和毫米波 (MM) 集成电路 (IC) 仍然是芯片设计人员面临的难题。设计新的并行和可扩展的计算算法，可以释放基于 GPU 的并行计算技术的潜力，变得非常可取。 本研究旨在研究新的并行仿真方法，以解决基于计算机上的单节点通用 GPU 或网络 GPU（GPU 集群）的大规模互连电路和模拟/RF/MM 集成电路。 首先，PI 将研究基于结构化互连电路分析解决方案的新并行仿真算法，例如 GPU 或 GPU 集群上的片上供电和时钟分配网络。 其次，PI 建议开发一种非常有效的数值并行仿真算法来分析通用互连。新算法将降低电路复杂度以提高效率。 PI 团队将研究如何并行化该方法中的主要计算步骤。第三，PI计划为高频电路（RF/MM）开发新的并行射击牛顿法。新方法将探索结构化 Krylov 子空间和基于 GPU 的并行化，以提高 RF/MM 集成电路仿真的效率和收敛性。这项研究的成果将极大地丰富 GPU 和 GPU 集群系统上线性和非线性动态系统并行数值分析的核心知识。通过与行业合作伙伴合作，PI 希望对设计界产生直接影响，以提高纳米 VLSI 系统的设计生产力。 研究结果还将帮助电子设计自动化 (EDA) 社区在探索当前和未来的通用 GPU 方面获得更多见解，以便在 GPU 和多核系统上并行化整个 EDA 工具。 拟议研究和相关培训的跨学科性质将使学生在竞争激烈的高科技就业市场中获得关键技能。这笔赠款将使 PI 能够雇用更多女性和代表性不足的少数族裔学生，进一步为美国科技劳动力的多样性做出贡献。