SHF: Small: Using Software Defined Cache to Accelerate Index Search for In-memory Applications: Software and Hardware Approaches

SHF：小型：使用软件定义的缓存来加速内存应用程序的索引搜索：软件和硬件方法

• 批准号: 1815303
• 负责人: Song Jiang
• 金额: $ 34.5万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815303&HistoricalAwards=false
• 关键词: SHF; Small; Using; Software; Defined

Memory is one of the most important components in large-scale data centers. As many software systems for big data processing keep their data sets entirely in memory to enable high-performance in-memory computing, memory efficiency becomes critical to application performance. While the applications, such as database systems and big data analytics, often serve as an infrastructure for information processing and providing IT services for millions of people in our society, improvement of their performance via optimization of the memory access is of great importance and impact. As memory access is slow compared with processor and cache speeds, the project eliminates unnecessary memory accesses with a re-designed cache architecture supporting flexible access and efficient management. In addition, this project provides research training to both undergraduate and graduate students, especially under-represented minority students, to prepare them to be future information technology professionals with strong skills in computer architecture and system areas. In memory-intensive computing, a significant percentage of memory access is spent on indices for translating user-defined keys into memory addresses for data accessing. However, due to lack of temporal and spatial localities, it can be very difficult to cache the indices and receive high cache-hit ratio. Accordingly, searching of the indexes is often at the memory speed, and searching for a data item may require multiple memory accesses. This project designs a software-defined cache -- an informed use of processor cache where a user program can explicitly specify data items for caching with their defined keys. As a two-phase effort, the project adopts a software approach, in which it is presented as a user-level library managing a look-aside buffer implicitly mapped into the cache, and a hardware approach, in which keys are explicitly hashed into the cache. Both approaches well exploit access locality and perform index search at the cache speed with their respective unique advantages. Accordingly, performance of memory system and memory-intensive applications can be significantly improved.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

内存是大型数据中心最重要的组件之一。由于许多用于大数据处理的软件系统将其数据集完全保存在内存中以实现高性能内存计算，因此内存效率对于应用程序性能变得至关重要。虽然数据库系统和大数据分析等应用程序通常作为信息处理的基础设施并为社会上数百万人提供 IT 服务，但通过优化内存访问来提高其性能具有重要意义和影响。由于与处理器和缓存速度相比，内存访问速度较慢，因此该项目通过重新设计的缓存架构来消除不必要的内存访问，支持灵活的访问和高效的管理。此外，该项目还为本科生和研究生，特别是少数族裔学生提供研究培训，帮助他们成为未来在计算机架构和系统领域拥有强大技能的信息技术专业人员。 在内存密集型计算中，内存访问的很大一部分花费在索引上，用于将用户定义的键转换为内存地址以进行数据访问。然而，由于缺乏时间和空间局部性，缓存索引并获得高缓存命中率可能非常困难。因此，索引的搜索通常以存储器速度进行，并且搜索数据项可能需要多次存储器访问。该项目设计了一个软件定义的缓存——处理器缓存的明智使用，其中用户程序可以使用其定义的键显式指定用于缓存的数据项。作为一个两阶段的工作，该项目采用了一种软件方法，其中它被呈现为一个用户级库，管理隐式映射到缓存中的后备缓冲区，以及一种硬件方法，其中密钥被显式散列到缓存中。两种方法都很好地利用了访问局部性，并以缓存速度执行索引搜索，具有各自独特的优势。因此，内存系统和内存密集型应用程序的性能可以得到显着提高。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

ChameleonDB: a key-value store for optane persistent memory

• DOI: 10.1145/3447786.3456237
• 发表时间: 2021-04
• 期刊: Proceedings of the Sixteenth European Conference on Computer Systems
• 作者: Wenhui Zhang;Xingsheng Zhao;Song Jiang;Hong Jiang

REMIX: Efficient Range Query for LSM-trees

• 发表时间: 2020-10
• 期刊: ArXiv
• 作者: Wenshao Zhong;Chen Chen-Chen;Xingbo Wu;Song Jiang

Wormhole: A Fast Ordered Index for In-memory Data Management

Wormhole：用于内存数据管理的快速有序索引

• DOI: 10.1145/3302424.3303955
• 发表时间: 2019
• 期刊: EuroSys '19 Proceedings of the Fourteenth EuroSys Conference 2019
• 作者: Wu, Xingbo;Ni, Fan;Jiang, Song
• 通讯作者: Jiang, Song

SS-CDC: a two-stage parallel content-defined chunking for deduplicating backup storage

• DOI: 10.1145/3319647.3325834
• 发表时间: 2019-05
• 期刊: Proceedings of the 12th ACM International Conference on Systems and Storage
• 作者: Fan Ni;Xing Lin;Song Jiang

Buffered Hash Table: Leveraging DRAM to Enhance Hash Indexes in the Persistent Memory

• DOI: 10.1109/nvmsa56066.2022.00011
• 发表时间: 2022-08
• 期刊: 2022 IEEE 11th Non-Volatile Memory Systems and Applications Symposium (NVMSA)
• 作者: Chen Zhong;Prajwal Challa;Xingsheng Zhao;Song Jiang