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有效的车辆发动机和发电燃烧器，工程师需要下一代计算建模工具。准确地描述常规和替代液体运输燃料的化学反应对于预测有害排放水平和其他重要数量至关重要，但是化学详细模型的高计算成本构成了设计师使用的重大障碍。为了使用这种准确的模型，需要在实用模拟中有效处理化学反应的软件。这个协作项目旨在开发此类工具，利用现代并行中央处理单元（CPU）和图形处理单元（GPU）的计算能力。除了帮助设计师创造清洁有效的发动机技术外，该项目的进步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，生物化学网络，甚至心脏电生理学，甚至努力的目标是使用COPCOCTERIENS来使用COPCOCERATER，该计算机是针对GPUS，CPUS，CPUS，CPUS，CPUS，CPUS，CPUS，CPUS，COP，CPUS，CPUS，CPUS，CPUS，CPUS，CPUS的其他许多方面的目标，该目标的目的。详细的化学和实现高保真性能但负担得起的反应性流量模拟。这将通过（1）（1）开发和比较在CPU和GPU/GPU/加速器上并行操作的化学动力学整合算法，（2）开发一种方法来检测一种方法，该方法由于化学动力学而自适应地选择了基于可用的硬件的最有效的函数，（（3）将（3）实现的组合策略（3），（3）实现（3）的效率效率（3），（3）4）使用商业和开源计算流体动力学代码进行模拟反应流动的元素，以及（5）根据上述软件元素设计便携式且可持续的软件库，包括建立用户社区。该程序的结果将是一个开源软件库，可大大降低在现实燃烧模拟中使用详细，准确的化学反应的成本；该计划的成功将基于实现刻板级绩效改善或更好地确定。