EAGER: Collaborative Research: Using PDE Descriptions To Generate Code Precisely Tailored To Energy-Constrained Systems Including Large GPU Accelerated Clusters

EAGER：协作研究：使用偏微分方程描述生成专门针对能源受限系统（包括大型 GPU 加速集群）定制的代码

• 批准号: 1265449
• 负责人: Steven Brandt
• 金额: $ 17万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1265449&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Using; PDE

Modern computer system architectures are forcing computational scientists to move scientific applicationsfrom traditional homogeneous cpu-based systems to heterogeneous multi-core/accelerator architectures. Obtaining performance in the presence of accelerators requires close attention tothe memory hierarchy and chip-level parallelism to reach even a modest fractionof the potential performance. As a result, coding tasks which were once the province oflone graduate students in a single discipline now require interdisciplinary teams of people. Project Chemora will explore the design of a new application framework for automaticallycreating highly optimized code for high-end computational machines. The systemwill use as input a set of partial differential equations (PDEs) that describe aproblem, it will then construct a machine-specific abstract performance model, and using theseit will generate well-tuned code and execution configurations for accelerated(e.g., hybrid CPU/GPU) computing clusters at various scales. Chemora willimprove programmability in this simplified domain by decoupling the science andcomputer science at a high level, thereby reducing the complexity and number of issues scientists need tocollectively understand and allowing individual scientists in the team to focus on their area ofspecialty. Chemora will improve performance (both wallclock time and energy) forsystems with both simple and complex sets of equations by making use of detailedinformation describing the problem and machine, and will provide improved loadbalancing through the AMPI framework.The Chemora project has chosen the Einstein equations as the primary science driver becausethese equations are one of the more complex PDE systems, one with manyhundreds of terms, and a problem scale that is challenging to optimize for mostcompilers. Achieving this vision for a general scientific problem would indeedbe a "Grand Challenge" in computational science, but in order to give ourresearch a sharper focus we have chosen as a science driver thesimulation of Intermediate mass ratio Binary Black Hole (IBBH) systems. Suchsystems, consisting of a black hole of mass 100 to 1,000 solar masses orbited bya smaller black hole of mass 5 to 20 solar masses are expected to be importantsources of gravitational waves for advanced Laser Interferometer GravitationalWave Observatory (LIGO) and the Einstein Telescope (ET). Accurate modeling ofthe waveforms from IBBH systems will be necessary in order to extractgravitational wave signals using template-matching data analysis techniques.

现代计算机系统架构迫使计算科学家将科学应用程序从传统的同构的基于cpu的系统转移到异构的多核/加速器架构。在存在加速器的情况下获得性能需要密切关注内存层次结构和芯片级并行性，以达到潜在性能的很小一部分。因此，编码任务曾经是一个单一学科的研究生的省，现在需要跨学科的团队。Chemora项目将探索设计一个新的应用程序框架，用于为高端计算机器自动创建高度优化的代码。系统将使用一组描述问题的偏微分方程（pde）作为输入，然后构建一个特定于机器的抽象性能模型，并使用这些模型生成经过良好调优的代码和执行配置，以加速(例如。（混合CPU/GPU）计算集群在不同规模。Chemora将通过将科学和计算机科学在高水平上解耦，从而提高这个简化领域的可编程性，从而降低科学家需要集体理解的问题的复杂性和数量，并允许团队中的单个科学家专注于他们的专业领域。Chemora将通过利用描述问题和机器的详细信息，提高具有简单和复杂方程组的系统的性能（包括时钟时间和能量），并将通过AMPI框架提供改进的负载平衡。Chemora项目选择爱因斯坦方程作为主要的科学驱动程序，因为这些方程是更复杂的PDE系统之一，有数百个项，对大多数编译器来说，优化问题规模是具有挑战性的。对于一个普遍的科学问题，实现这一愿景确实是计算科学中的一个“重大挑战”，但为了使我们的研究有一个更清晰的焦点，我们选择了中间质量比双黑洞（IBBH）系统作为科学驱动模拟。这样的系统由一个质量为100到1000太阳质量的黑洞和一个质量为5到20太阳质量的较小黑洞组成，预计将成为先进激光干涉仪引力波天文台（LIGO）和爱因斯坦望远镜（ET）的重要引力波源。为了使用模板匹配数据分析技术提取引力波信号，必须对IBBH系统的波形进行精确建模。