XPS: FULL: CCA: NUMB: Exploiting Non-Uniform Memory Bandwidth for Computational Science

XPS：FULL：CCA：NUMB：利用非均匀内存带宽进行计算科学

• 批准号: 1533885
• 负责人: David Wood
• 金额: $ 75万
• 依托单位: University of Wisconsin-Madison
• 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 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533885&HistoricalAwards=false
• 关键词: XPS; FULL; CCA; NUMB; Exploiting

This research seeks to maximize the benefit that modern and upcoming trends in computer hardware systems can confer on science and engineering disciplines that use computation to catalyze discovery and innovation. Computer architecture and hardware systems have been experiencing disruptive transformations, and are certain to continue on this evolutionary path as platforms highlighted by massive parallelism and heterogeneous composition of processing units are becoming the norm. Such advances have triggered a realignment of market segments and computing capabilities from what was previously known: Complex fluid simulations that were previously in the purview of enterprise-grade computing may now be accommodated in modestly sized clusters. Virtual prototyping tasks previously handled by clusters can now be performed using a single workstation. This newfound availability of advanced computer capabilities, however, poses challenges for traditional practices in scalable software engineering, and even reaches the limitations of theory and algorithms that were designed with a less-parallel, homogeneous computing platform in mind. This research activity combines one of the most prominent ongoing trends in computer architecture, namely the fact that memory access and bandwidth are both non-uniform in heterogeneous CPU/GPU systems, with driving applications from the domain of computational science and engineering. This project will lead to the coordinated development of hardware innovations, scalable development practices, heterogeneity-friendly distributed computing algorithms and parallelism oriented numerical methods to extract optimal performance from emerging platforms on scientific computing workloads. The expected systems and computer architecture advances resulting from this activity include: (i) Defining appropriate consistency models for systems with Non-Uniform Memory Bandwidth (NUMB), developing and refining Heterogeneous Race Free (HRF) consistency models for overlapping scopes and NUMB platforms. (ii) Improving the coordination of memory bandwidth utilization, by developing interfaces and mechanisms to manage interstage temporal locality, and assessing such mechanisms and policies in the context of our driving applications. (iii) Exploring enhanced mechanisms for synchronization, both among GPUs as well as between the CPU and GPU, by exploiting hardware-assisted reduction models in heterogeneous system and provide fine-grain data handling and synchronization semantics without fine-scale locks and barriers. (iv) Designing and implementing Operating System extensions to better manage heterogeneous memory (e.g., various levels of caches, accelerator memory or non-volatile memory (NVM) data stores). Applications research will emphasize (a) workloads in Adaptive Computational Fluid Dynamics (CFD), an area that has traditionally fostered co-development of theoretical and systems aspects and is well represented in the joint expertise of the collaborating investigators, and (b) instances of interactive Virtual Anatomical Modeling and Simulation, as an emerging exemplar of a host of computer-aided training tools for medical and surgical education that has only been made possible due to the enhanced capacity of modern computing platforms. This synergistic research endeavor will facilitate important emerging applications in medicine, computer-aided design and engineering research that are on the verge of attaining practical utility. It will also reveal new options for enabling more energy-efficient systems, provide a blueprint for optimized performance in throughput-sensitive applications beyond numerical computing, and generate opportunities for development of academic courses that highlight the merits of a conscious co-evolution of systems and theory principles.

这项研究旨在最大限度地利用计算机硬件系统的现代和即将到来的趋势为利用计算促进发现和创新的科学和工程学科带来的好处。计算机体系结构和硬件系统一直在经历颠覆性的变革，并且随着以大规模并行性和处理单元异构组成为特色的平台正在成为常态，并且肯定会继续沿着这条演进道路继续前进。这些进步引发了市场细分和计算能力的重新调整：以前属于企业级计算范围的复杂流体模拟现在可以容纳在中等规模的集群中。以前由集群处理的虚拟原型任务现在可以使用单个工作站来执行。然而，这种新发现的先进计算机功能对可扩展软件工程的传统实践提出了挑战，甚至达到了在设计时考虑并行度较低的同质计算平台的理论和算法的局限性。这项研究活动结合了计算机体系结构中最突出的持续趋势之一，即异构 CPU/GPU 系统中内存访问和带宽均不一致的事实，并推动了计算科学和工程领域的应用。该项目将导致硬件创新、可扩展开发实践、异构友好的分布式计算算法和面向并行的数值方法的协调发展，以从科学计算工作负载的新兴平台中提取最佳性能。这项活动带来的预期系统和计算机架构进步包括：(i) 为具有非均匀内存带宽 (NUMB) 的系统定义适当的一致性模型，为重叠范围和 NUMB 平台开发和完善异构无竞争 (HRF) 一致性模型。 (ii) 通过开发管理级间时间局部性的接口和机制，并在我们的驾驶应用程序的背景下评估此类机制和策略，改善内存带宽利用的协调。 (iii) 通过利用异构系统中的硬件辅助缩减模型，探索 GPU 之间以及 CPU 和 GPU 之间的增强同步机制，并提供细粒度数据处理和同步语义，而无需细粒度锁和屏障。 (iv) 设计和实现操作系统扩展，以更好地管理异构内存（例如，各种级别的缓存、加速器内存或非易失性内存（NVM）数据存储）。应用研究将强调（a）自适应计算流体动力学（CFD）的工作量，该领域传统上促进理论和系统方面的共同开发，并在合作研究人员的联合专业知识中得到充分体现，以及（b）实例交互式虚拟解剖建模和模拟，作为医学和外科教育的一系列计算机辅助培训工具的新兴范例，只有现代计算平台能力的增强才使其成为可能。这项协同研究工作将促进医学、计算机辅助设计和工程研究方面的重要新兴应用，这些应用即将获得实际应用。它还将揭示实现更节能系统的新选项，为除数值计算之外的吞吐量敏感型应用程序提供优化性能的蓝图，并为开发学术课程创造机会，这些课程强调系统和系统有意识地共同进化的优点。理论原则。