II-NEW: An Experimental Platform for Investigating Energy-Performance Tradeoffs for Systems with Deep Memory Hierarchies

II-新：用于研究具有深度内存层次结构的系统的能源性能权衡的实验平台

• 批准号: 1305375
• 负责人: Manish Parashar
• 金额: $ 30万
• 依托单位: Rutgers University New Brunswick
• 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: 2013-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1305375&HistoricalAwards=false
• 关键词: II; NEW; Experimental; Platform; Investigating

As the scale and complexity of computing and data infrastructures supporting science and engineering grow, power costs are becoming important concerns in terms of costs, reliability and overall sustainability. As a result, it is becoming increasingly important to understand power/performance behaviors and tradeoffs from an application perspective for emerging system configuration, i.e., those with multiple cores, deep memory hierarchies and accelerators. This project builds an instrumented experimental platform that supports such an understanding, and enables research and training activities in this area. Specifically, the proposed experimental platform is composed of nodes with a deep memory architecture that contains four different levels: DRAM, PCIe-based non-volatile memory, solid-state drive and spinning hard disk, in addition to accelerators. Power metering is deployed as part of the infrastructure.The experimental platform enables the experimental exploration of the power/performance behaviors of large scale computing systems and datacenters as well as compute and data intensive application they support, and uniquely supports research toward understanding the management and optimization of these systems and applications. It also enables research in multiple areas, including: application-aware cross-layer management, power-performance tradeoffs for data-intensive scientific workflows and thermal implications of deep memory hierarchies in virtualized Cloud environments. Data and compute intensive applications are becoming increasingly critical to a wide range of domains, and the ability to develop large-scale and sustainable platforms and software infrastructure to support these applications will have significant impact in driving research and innovations in these domains. The developed experimental platform enables key research activities to support this. It provides important insights that will impact the realization and sustainability of very large-scale infrastructures necessary for current and emerging data and compute intensive applications. The infrastructure also provides an important infrastructure for education and training in different areas related to power management, energy efficiency, data management, memory management, and virtualization.

随着支持科学和工程的计算和数据基础设施的规模和复杂性不断增长，电力成本正成为成本、可靠性和整体可持续性方面的重要问题。因此，从应用角度理解新兴系统配置（即具有多核、深存储器层次结构和加速器的系统配置）的功耗/性能行为和权衡变得越来越重要。该项目建立了一个仪器化实验平台来支持这种理解，并促进该领域的研究和培训活动。具体来说，所提出的实验平台由具有深度内存架构的节点组成，除了加速器之外，该架构还包含四个不同级别：DRAM、基于 PCIe 的非易失性内存、固态驱动器和旋转硬盘。电能计量作为基础设施的一部分进行部署。该实验平台能够对大规模计算系统和数据中心以及它们支持的计算和数据密集型应用程序的功耗/性能行为进行实验探索，并独特地支持旨在了解管理和数据中心的研究。优化这些系统和应用程序。它还支持多个领域的研究，包括：应用程序感知的跨层管理、数据密集型科学工作流程的功耗性能权衡以及虚拟化云环境中深度内​​存层次结构的热影响。数据和计算密集型应用程序对广泛的领域变得越来越重要，开发大规模且可持续的平台和软件基础设施来支持这些应用程序的能力将对推动这些领域的研究和创新产生重大影响。开发的实验平台使关键研究活动能够支持这一点。它提供了重要的见解，将影响当前和新兴数据和计算密集型应用程序所需的超大规模基础设施的实现和可持续性。该基础设施还为电源管理、能源效率、数据管理、内存管理和虚拟化等不同领域的教育和培训提供了重要的基础设施。

项目成果

Exploring Power Budget Scheduling Opportunities and Tradeoffs for AMR-based Applications

探索基于 AMR 的应用的功率预算调度机会和权衡

• DOI: 10.1109/sbac-pad.2018.00023
• 发表时间: 2018-01
• 期刊: 2018 30th International Symposium on Computer Architecture and High Performance Computing
• 作者: Qin, Yubo;Rodero, Ivan;Subedi, Pradeep;Parashar, Manish;Rigo, Sandro
• 通讯作者: Rigo, Sandro

Exploring the Potential of Next Generation Software-Defined In-Memory Frameworks

探索下一代软件定义内存框架的潜力

• DOI: 10.1109/sbac-pad.2018.00042
• 发表时间: 2018-01
• 期刊: 2018 30th International Symposium on Computer Architecture and High Performance Computing
• 作者: Chen, Shouwei;Rodero, Ivan
• 通讯作者: Rodero, Ivan

Understanding Behavior Trends of Big Data Frameworks in Ongoing Software-Defined Cyber-Infrastructure

了解正在进行的软件定义网络基础设施中大数据框架的行为趋势

• DOI: 10.1145/3148055.3148079
• 发表时间: 2017-01
• 期刊: Applications and Technologies
• 作者: Chen, Shouwei;Rodero, Ivan
• 通讯作者: Rodero, Ivan

Persistent Data Staging Services for Data Intensive In-situ Scientific Workflows

适用于数据密集型原位科学工作流程的持久数据暂存服务

• DOI: 10.1145/2912152.2912157
• 发表时间: 2016-01
• 期刊: Proceedings of the ACM International Workshop on Data-Intensive Distributed Computing
• 作者: Romanus, Melissa;Klasky, Scott;Chang, Choong;Rodero, Ivan;Zhang, Fan;Jin, Tong;Sun, Qian;Bui, Hoang;Parashar, Manish;Choi, Jong;et al
• 通讯作者: et al