CSR: Medium: Collaborative Research: Workload-Aware Storage Architectures for Optimal Performance and Energy Efficiency

CSR：中：协作研究：实现最佳性能和能源效率的工作负载感知存储架构

• 批准号: 1302246
• 负责人: Erez Zadok
• 金额: $ 51.39万
• 依托单位: SUNY at Stony Brook
• 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 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1302246&HistoricalAwards=false
• 关键词: CSR; Medium; Collaborative; Research; Workload

The most significant performance and energy bottlenecks in a computer areoften caused by the storage system, because the gap between storage deviceand CPU speeds is greater than in any other part of the machine. Big dataand new storage media only make things worse, because today's systems arestill optimized for legacy workloads and hard disks. The team at StonyBrook University, Harvard University, and Harvey Mudd College has shown thatlarge systems are poorly optimized, resulting in waste that increasescomputing costs, slows scientific progress, and jeopardizes the nation'senergy independence.First, the team is examining modern workloads running on a variety ofplatforms, including individual computers, large compute farms, and anext-generation infrastructure, such as Stony Brook's Reality Deck, amassive gigapixel visualization facility. These workloads produce combinedperformance and energy traces that are being released to the community.Second, the team is applying techniques such as statistical featureextraction, Hidden Markov Modeling, data-mining, and conditional likelihoodmaximization to analyze these data sets and traces. The Reality Deck isused to visualize the resulting multi-dimensional performance/energy datasets. The team's analyses reveal fundamental phenomena and principles thatinform future designs.Third, the findings from the first two efforts are being combined to developnew storage architectures that best balance performance and energy underdifferent workloads when used with modern devices, such as solid-statedrives (SSDs), phase-change memories, etc. The designs leverage the team'swork on storage-optimized algorithms, multi-tier storage, and new optimizeddata structures.

计算机中最重要的性能和能源瓶颈通常是由存储系统引起的，因为存储设备和CPU速度之间的差距比机器的任何其他部分都大。 大数据和新的存储介质只会让事情变得更糟，因为今天的系统仍然针对传统工作负载和硬盘进行了优化。 石溪大学、哈佛大学和哈维马德学院的研究小组已经证明，大型系统优化不力，导致浪费，增加了计算成本，减缓了科学进步，并危及国家的能源独立。首先，研究小组正在研究在各种平台上运行的现代工作负载，包括个人计算机、大型计算农场和下一代基础设施，如斯托尼布鲁克的Reality Deck，巨大的千兆像素可视化设备。 这些工作负载产生了正在向社区发布的综合性能和能量跟踪。其次，该团队正在应用统计特征提取、隐马尔可夫模型、数据挖掘和条件似然最大化等技术来分析这些数据集和跟踪。 Reality Deck用于可视化多维性能/能源数据集。 该团队的分析揭示了指导未来设计的基本现象和原则。第三，将前两项工作的发现结合起来，开发新的存储架构，当与现代设备（如固态硬盘（SSD），相变存储器等）一起使用时，该架构可以在不同工作负载下最佳地平衡性能和能量。设计利用了该团队在存储优化算法，多层存储，and new新optimized优化data数据structures结构.