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

现代计算机系统架构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多核/加速器架构。 在加速器的存在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的一个研究生的省，现在需要跨学科的团队的人。Project Chemora将探索一个新的应用程序框架的设计，用于自动为高端计算机器创建高度优化的代码。该系统将使用一组描述问题的偏微分方程（PDE）作为输入，然后将构建一个机器特定的抽象性能模型，并使用这些模型生成优化的代码和执行配置，以加速（例如，混合CPU/GPU）计算集群。 Chemora将在这个简化的领域通过在高层次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（包括挂钟时间和能量），通过利用描述问题和机器的详细信息，为具有简单和复杂方程组的系统提供改进的负载平衡，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，有数百个术语，and a problem问题scale规模that is challenging挑战to optimize优化for mostcompilers编译器.实现这一愿景的一般科学问题确实将是一个“大挑战”在计算科学，但为了给我们的研究一个更清晰的焦点，我们选择了作为一个科学驱动程序的模拟中间质量比二元黑洞（IBBH）系统。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。为了利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。

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.