SI2-SSE: Collaborative Research: An Intelligent and Adaptive Parallel CPU/GPU Co-Processing Software Library for Accelerating Reactive-Flow Simulations

SI2-SSE：协作研究：用于加速反应流仿真的智能自适应并行 CPU/GPU 协同处理软件库

• 批准号: 1534688
• 负责人: Chih-Jen Sung
• 金额: $ 21.44万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1534688&HistoricalAwards=false
• 关键词: SI2; SSE; Collaborative; Research; Intelligent

In order to develop the next generation of clean and efficient vehicle engines and power-generating combustors, engineers need the next generation of computational modeling tools. Accurately describing the chemistry of conventional and alternative liquid transportation fuels is vital to predict harmful emission levels and other important quantities, but the high computational cost of detailed models for chemistry poses a significant barrier to use by designers. In order to use such accurate models, software is needed that can efficiently handle chemistry in practical simulations. This collaborative project aims to develop such tools, employing the computational power of modern parallelized central processing units (CPUs) and graphics processing units (GPUs). In addition to helping designers create clean and efficient engine technology, the advances made in this project are widely applicable to other computational modeling problems including astrophysics, nuclear reactions, atmospheric chemistry, biochemical networks, and even cardiac electrophysiology.The objective of the proposed effort is to develop software elements specifically targeted at co-processing on GPUs, CPUs, and other many-core accelerator devices to reduce the computational cost of using detailed chemistry and enable high-fidelity yet affordable reactive-flow simulations. This will be achieved by (1) developing and comparing chemical kinetics integration algorithms for parallel operation on CPUs and GPUs/accelerators, (2) developing a method for detecting local stiffness due to chemical kinetics and adaptively selecting the most efficient solver based on available hardware, (3) implementing a computational cell clustering strategy to group similar spatial locations, (4) demonstrating the improved performance offered by these software elements using commercial and open-source computational fluid dynamics codes for modeling reactive flows, and (5) designing a portable and sustainable software library based on the above software elements, including building a community of users. The result of this program will be an open source software library that significantly decreases the cost of using detailed, accurate chemistry in realistic combustion simulations; the success of the program will be determined based on achieving order-of-magnitude performance improvement or better.

为了开发下一代清洁高效的汽车发动机和发电燃烧室，工程师需要下一代计算建模工具。准确描述传统和替代液体运输燃料的化学性质对于预测有害排放水平和其他重要量至关重要，但化学详细模型的高计算成本对设计人员的使用构成了重大障碍。为了使用这种精确的模型，需要能够在实际模拟中有效处理化学的软件。这个合作项目旨在开发这样的工具，利用现代并行中央处理器（CPU）和图形处理器（GPU）的计算能力。除了帮助设计人员创造清洁高效的引擎技术外，该项目所取得的进展还广泛适用于其他计算建模问题，包括天体物理学、核反应、大气化学、生化网络，甚至心脏电生理学。该项目的目标是开发专门针对GPU、CPU、和其他多核加速器设备，以降低使用详细化学的计算成本，并实现高保真但负担得起的反应流模拟。这将通过以下方式实现：（1）开发和比较用于在CPU和GPU/加速器上并行操作的化学动力学积分算法，（2）开发用于检测由于化学动力学引起的局部刚度的方法，并基于可用硬件自适应地选择最有效的求解器，（3）实施计算单元聚类策略以将相似的空间位置分组，（4）使用商业和开放源代码计算流体动力学代码对反应流进行建模，展示这些软件元素提供的改进性能，以及（5）基于上述软件元素设计可移植和可持续的软件库，包括建立用户社区。该计划的结果将是一个开源软件库，大大降低了在真实燃烧模拟中使用详细，准确的化学物质的成本;该计划的成功将取决于实现数量级性能改进或更好。