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: 1265434
• 负责人: David Bader
• 金额: $ 10万
• 依托单位: Georgia Tech Research Corporation
• 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 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1265434&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Using; PDE

Modern computer system architectures are forcing computational scientists to move scientific applications from traditional homogeneous cpu-based systems to heterogeneous multi-core/accelerator architectures. Obtaining performance in the presence of accelerators requires close attention to the memory hierarchy and chip-level parallelism to reach even a modest fraction of the potential performance. As a result, coding tasks which were once the province of lone graduate students in a single discipline now require interdisciplinary teams of people. Project Chemora will explore the design of a new application framework for automatically creating highly optimized code for high-end computational machines. The system will use as input a set of partial differential equations (PDEs) that describe a problem, it will then construct a machine-specific abstract performance model, and using these it will generate well-tuned code and execution configurations for accelerated (e.g., hybrid CPU/GPU) computing clusters at various scales. Chemora will improve programmability by decoupling the science and computer science at a high level, thereby reducing the complexity and number of issues scientists need to collectively understand and allowing individual scientists in the team to focus on their area of specialty. Chemora will improve performance (both wallclock time and energy) for systems with both simple and complex sets of equations by making use of detailed information describing the problem and machine, and will provide improved load balancing through the AMPI framework.The Chemora project has chosen the Einstein equations as the primary science driver because these equations are one of the more complex PDE systems, one with many hundreds of terms, and a problem scale that is challenging to optimize for most compilers. Achieving this vision for a general scientific problem would indeed be a "Grand Challenge" in computational science, but in order to give the research a sharper focus the project will focus on the simulation of Intermediate mass ratio Binary Black Hole (IBBH) systems. Such systems, consisting of a black hole of mass 100 to 1,000 solar masses orbited by a smaller black hole of mass 5 to 20 solar masses are expected to be important sources of gravitational waves for advanced Laser Interferometer Gravitational Wave Observatory (LIGO) and the Einstein Telescope (ET). Accurate modeling of the waveforms from IBBH systems will be necessary in order to extract gravitational wave signals using template-matching data analysis techniques.

现代计算机系统架构正迫使计算科学家将科学应用从传统的基于CPU的同构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存储器层次结构和芯片级并行性，以达到甚至是潜在性能的适度部分。因此，编码任务曾经是一个学科中唯一的研究生的领域，现在需要跨学科的团队。Project Chemora将探索一个新的应用程序框架的设计，用于为高端计算机器自动创建高度优化的代码。该系统将使用描述问题的一组偏微分方程（PDE）作为输入，然后它将构建机器特定的抽象性能模型，并且使用这些模型，它将生成良好调优的代码和执行配置，以用于加速（例如，混合CPU/GPU）计算集群。 Chemora将通过在高水平上解耦科学和计算机科学来提高可编程性，从而减少科学家需要共同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将提高性能Chemora项目选择爱因斯坦方程作为主要的科学驱动力，因为这些方程是更复杂的PDE系统之一，有数百个术语，以及对于大多数编译器优化具有挑战性的问题规模。实现这一普遍科学问题的愿景确实是计算科学中的一个“巨大挑战”，但为了使研究更加突出重点，该项目将重点放在中等质量比二元黑洞（IBBH）系统的模拟上。这样的系统由一个质量为100至1，000太阳质量的黑洞组成，由一个质量为5至20太阳质量的较小黑洞围绕，预计将成为先进的激光干涉引力波天文台（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，需要对IBBH系统的波形进行精确建模。