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提高计算机硬件系统的现代和即将到来的趋势可以赋予使用计算来催化发现和创新的科学和工程学科的好处。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的异构组成为突出特征的平台正在成为常态，它们肯定会继续沿着这条进化道路前进。这些进步引发了细分市场和计算能力的重新调整，而这与以前所知的不同：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执行。然而，这种新发现的先进计算机功能的可用性对可扩展软件工程的传统实践提出了挑战，甚至达到了理论和算法的局限性，这些理论和算法是根据较少并行的同构计算平台设计的。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，即内存访问和带宽在异构CPU/GPU系统中都是不均匀的，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，可扩展的开发实践，异构性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。预期这项活动将带来的系统和计算机结构方面的进展包括：（i）为具有非统一内存带宽的系统确定适当的一致性模型，为重叠范围和非统一内存带宽平台开发和改进异构无竞争一致性模型。(ii)通过开发接口和机制来管理阶段间的时间局部性，并在我们的驱动应用程序的上下文中评估这些机制和策略，来改善内存带宽利用率的协调。(iii)通过利用异构系统中的硬件辅助约简模型，探索GPU之间以及CPU和GPU之间的增强同步机制，并提供细粒度的数据处理和同步语义，而无需细尺度的锁和屏障。(iv)设计和实现操作系统扩展以更好地管理异构内存（例如，各种级别的高速缓存、加速器存储器或非易失性存储器（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，该领域传统上促进了理论和系统方面的共同发展，并在合作研究人员的联合专业知识中得到了很好的体现，以及（B）交互式虚拟解剖建模和仿真的实例，作为一个新兴的典范，主机的计算机辅助培训工具，医疗和外科教育，只有成为可能，由于增强能力的现代计算平台。这种协同研究奋进将促进医学，计算机辅助设计和工程研究中的重要新兴应用，这些应用即将获得实际效用。它还将揭示实现更节能系统的新选项，为数字计算之外的吞吐量敏感型应用提供优化性能的蓝图，并为开发学术课程提供机会，突出系统和理论原则的有意识共同进化的优点。