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: 1265451
• 负责人: Gengbin Zheng
• 金额: $ 2.49万
• 依托单位: University of Illinois at Urbana-Champaign
• 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 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1265451&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.

现代计算机系统体系结构迫使计算科学家将科学应用从传统的基于同构CPU的系统转移到不同的多核/加速器体系结构。在加速器存在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，才能达到潜在性能的一小部分。因此，曾经只由一门学科的研究生完成的编码任务现在需要跨学科的团队。Chemora项目将探索设计一种新的应用程序框架，用于自动为高端计算机器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组(PDE)作为输入，然后它将构建特定于机器的抽象性能模型，并使用这些模型为不同规模的加速(例如，混合CPU/GPU)计算集群生成经过良好调整的代码和执行配置。Chemora将在高水平上分离科学和计算机科学，从而降低科学家集体理解的复杂性和问题的数量，并允许团队中的个别科学家专注于他们的专业领域，从而提高这一简化领域的可编程性。Chemora将通过利用描述问题和机器的详细信息来提高具有简单和复杂方程组的系统的性能(挂钟时间和能量)，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因素，因为这些方程是更复杂的PDE系统之一，有数百个项，并且问题规模对于大多数编译器来说是具有挑战性的优化。对于一个一般的科学问题，实现这一愿景确实是计算科学中的一项“巨大挑战”，但为了使我们的研究有一个更明确的重点，我们选择了中等质量比双黑洞(IBBH)系统的模拟作为科学驱动。这种由100到1000个太阳质量的黑洞和5到20个太阳质量的小黑洞组成的系统有望成为先进的激光干涉仪引力波天文台(LIGO)和爱因斯坦望远镜(ET)的重要引力波来源。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。