SHF: Small: Non-Uniformity--Centric Program Optimizations for Dynamic Computations on Chip Multiprocessors

SHF：小：片上多处理器动态计算的非均匀性以程序优化为中心

• 批准号: 1320796
• 负责人: Xipeng Shen
• 金额: $ 42.5万
• 依托单位: College of William and Mary
• 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 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320796&HistoricalAwards=false
• 关键词: SHF; Small; Non; Uniformity; Centric

This research project is motivated by a growing gap between trends in processor development and needs of modern data-intensive dynamic applications ranging from differential equation solvers to data mining tools to particle dynamics simulations, playing an essential role in science and humanity. These applications feature tremendous amounts of data accesses as well as complex patterns in data accesses or control flows. The properties make them a great challenge for modern processors which are evolving exactly opposite to these applications' needs: A chip's aggregate computing power is rapidly outgrowing memory bandwidth; the rise of throughput-oriented manycores makes system throughput even more sensitive to irregular computations. The new paradigm of program optimizations, non-uniformity--centric, distinctively takes the non-uniform inter-core relations in modern systems as the first-order constraint for program optimizations. The proposed framework named PipeReg is a new way to reorganize data accesses and threads during run time to reduce the influence of irregular computations on the throughput of massively parallel processors. In addition, a novel kind of program transformations, neighborhood-aware, exploiting non-uniform interactions among threads in on-chip storage in a multi-socket multicore system. Together, the two techniques will synergistically remove some important barriers for data-intensive dynamic applications to tap into the full power of future computing systems. The outcome from this research will provide essential support for enhancing the computing efficiency of data-intensive dynamic applications in the era of heterogeneous parallel systems. Because of the critical roles of these applications, this research will help foster sustained advancement in science, commerce, health, and other areas.

该研究项目的动机是处理器开发趋势与现代数据密集型动态应用程序的需求之间日益扩大的差距，这些应用程序从微分方程求解器到数据挖掘工具再到粒子动力学模拟，在科学和人类中发挥着重要作用。这些应用程序具有大量数据访问以及数据访问或控制流中的复杂模式。这些特性使它们对现代处理器构成了巨大的挑战，而现代处理器的发展与这些应用程序的需求完全相反：芯片的总计算能力正在迅速超过内存带宽；面向吞吐量的众核的兴起使得系统吞吐量对不规则计算更加敏感。以非均匀性为中心的程序优化新范式，独特地将现代系统中的非均匀核间关系作为程序优化的一阶约束。所提出的名为 PipeReg 的框架是一种在运行时重新组织数据访问和线程的新方法，以减少不规则计算对大规模并行处理器吞吐量的影响。此外，一种新颖的程序转换，邻域感知，利用多插槽多核系统中片上存储中线程之间的非均匀交互。这两种技术将协同消除数据密集型动态应用程序充分利用未来计算系统的一些重要障碍。该研究成果将为异构并行系统时代提高数据密集型动态应用的计算效率提供重要支撑。由于这些应用的关键作用，这项研究将有助于促进科学、商业、健康和其他领域的持续进步。