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.

该研究项目的促进是，从处理器开发的趋势与现代数据密集型动态应用的需求之间的差距越来越大，从微分方程求解器到数据挖掘工具再到粒子动力学模拟，在科学和人类中起着重要的作用。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。这些属性使它们成为现代处理器的巨大挑战，即与这些应用程序的需求完全相反的现代处理器：芯片的总计算能力迅速超过了内存带宽；以吞吐量为导向的多孔的兴起使系统吞吐量对不规则计算更加敏感。计划优化的新范式（以不均匀性为中心）独特地将现代系统中的不均匀核心关系视为程序优化的一阶约束。所提出的名为Pipereg的框架是在运行时间内重组数据访问和线程的一种新方法，以减少不规则计算对大量并行处理器吞吐量的影响。此外，一种新型的程序转换，邻里感知，利用多插入多机系统在片上存储中的线程之间的非均匀相互作用。这两种技术将共同消除数据密集型动态应用程序的一些重要障碍，以利用未来计算系统的全部功能。这项研究的结果将为提高异质平行系统时代数据密集型动态应用的计算效率提供基本支持。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发展。