Utilizing Memory Parallelism for High Performance Data Processing

利用内存并行性进行高性能数据处理

• 批准号: 1536079
• 负责人: Xian-He Sun
• 金额: $ 17.5万
• 依托单位: Illinois Institute of Technology
• 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: 2015-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1536079&HistoricalAwards=false
• 关键词: Utilizing; Memory; Parallelism; Performance; Data

While advances in microprocessor design continue to increase computing speed, improvements in data access speed of computing systems lag far behind. At the same time, data-intensive large-scale applications, such as information retrieval, computer animation, and big data analytics are emerging. Data access delay has become the vital performance bottleneck of modern high performance computing (HPC). Memory concurrency exists at each layer of modern memory hierarchies; however, conventional computing systems are primarily designed to improve CPU utilization and have inherent limitations in addressing the critical issue of data movement in HPC. In this research, the PI proposes the concept of memory parallelism and the customized parallel memory (CuPM) system architecture in order to address data movement bottleneck issues. CuPM is an architecture designed to exploit memory concurrency to support high performance data processing (HPDP). This new abstraction extracts the general principles of parallel memory system by building on the new Concurrent-AMAT metric, a set of fresh theoretical results in data access concurrency, a series of recent successes in parallel I/O optimization, and new technology opportunities. CuPM is a novel cross-layer and cross-cutting approach for exploring and utilizing current and future memory technologies based on opportunities arising from memory concurrency.Current HPC systems do not fully recognize or exploit the concurrency that modern memory systems provide. The key new idea of CuPM is to establish a systematic way of exploring, enhancing, utilizing, and customizing memory concurrency to build effective memory systems. It will be developed with two goals: to understand and reveal the memory concurrency and its properties, and to explore and utilize current memory concurrency for HPDP. The former will evaluate the potential of the latter; while the latter will verify and testing the former. Together, they enable CuPM to address a central challenge for extreme computing ? efficient memory systems. The proposed CuPM architecture has the potential to transform systems from application through runtime through architecture.

尽管微处理器设计的进步继续提高计算速度，但计算系统数据访问速度的提高远远落后。同时，出现了数据密集型大规模应用程序，例如信息检索，计算机动画和大数据分析。数据访问延迟已成为现代高性能计算（HPC）的重要性能瓶颈。内存并发存在于现代内存层次结构的每一层；但是，传统的计算系统主要旨在改善CPU利用率，并在解决HPC中数据流动的关键问题时具有固有的局限性。在这项研究中，PI提出了记忆并行性的概念和自定义的并行内存（CUPM）系统体系结构，以解决数据运动瓶颈问题。 CUPM是一种旨在利用内存并发来支持高性能数据处理（HPDP）的体系结构。这种新的抽象通过建立新的并发 - AMAT指标，一组数据访问并发的新理论结果，在并行I/O优化方面取得了一系列成功以及新的技术机会来提取并行记忆系统的一般原理。 CUPM是一种基于内存并发产生的机会来探索和利用当前和未来记忆技术的新型跨层和横切方法。电流HPC系统无法完全识别或利用现代记忆系统提供的并发。 CUPM的关键想法是建立一种系统的方法来探索，增强，利用和自定义内存并发以构建有效的内存系统。它将具有两个目标开发：了解和揭示内存并发性及其属性，并探索和利用HPDP的当前内存并发。前者将评估后者的潜力；后者将验证和测试前者。他们共同使CUPM能够应对极端计算的核心挑战？有效的内存系统。拟议的CUPM体系结构有可能将系统从应用程序转换为运行时到架构。