XPS: EXPL: Write Locality Theory and Optimization for Hybrid Memory

XPS：EXPL：混合内存的写入局部性理论和优化

• 批准号: 1629376
• 负责人: Chen Ding
• 金额: $ 30万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629376&HistoricalAwards=false
• 关键词: XPS; EXPL; Write; Locality; Theory

A limit on extreme scale computing is the capacity, speed, durability and most importantly, energy efficiency of memory. Persistent memory is a disruptive technology that drastically reduces memory cost and static power but introduces the problems of slow writes and limited write endurance. An effective solution is caching. However, existing caches have been designed for fast reads: they do not minimize the number of write backs from cache to memory. The intellectual merits of this project are sufficient theories and efficient techniques for optimal write caching in future systems that use persistent memory. Caching, in both software and hardware, is increasingly prevalent. Hardware caches serve most of the memory accesses on commodity machines as well as super computers. Software caches, e.g. Memcached used by Wikipedia and Facebook, serve most of the data requests from online users. The project's broader significance and importance are the broad benefits of cache optimization in scientific and consumer computing.This project develops the formal concept of write locality and the theory and techniques to characterize locality and optimize the cache. It has three parts. The first is write locality modeling, which includes a set of metrics that can derive the amount of modified data in cache and the write backs from cache to memory in cache of all sizes and degrees of sharing. The second is deductive optimization, which mathematically derives the cache performance of different task and data organizations and selects the best solution among a combinatorial number of choices. The last is system modeling and application optimization, which use write locality to model software and hardware cache and to optimize a program.

极限规模计算的限制是容量、速度、耐用性，最重要的是内存的能效。持久存储器是一种颠覆性技术，它大大降低了存储器成本和静态功耗，但也带来了写入速度慢和写入持久性有限的问题。一个有效的解决方案是缓存。然而，现有的高速缓存已经被设计用于快速读取：它们没有最小化从高速缓存到存储器的写回的数量。 这个项目的智力价值是足够的理论和有效的技术，在未来的系统中使用持久内存的最佳写缓存。 在软件和硬件中，缓存越来越普遍。硬件缓存服务于商品机器和超级计算机上的大部分内存访问。软件缓存，例如维基百科和Facebook使用的Memcached，为在线用户的大部分数据请求提供服务。 该项目更广泛的意义和重要性是高速缓存优化在科学和消费者计算中的广泛好处。该项目开发了写局部性的正式概念以及描述局部性和优化该高速缓存的理论和技术。 它有三个部分。 第一个是写局部性建模，它包括一组度量，可以导出缓存中修改的数据量以及所有大小和共享程度的缓存中从缓存到内存的写回。 第二种是演绎优化，它从数学上推导出不同任务和数据组织的该高速缓存性能，并在组合数量的选择中选择最佳解决方案。最后是系统建模和应用优化，利用写局部性对软件和硬件缓存进行建模和优化。

项目成果

Timescale functions for parallel memory allocation

• DOI: 10.1145/3315573.3329987
• 发表时间: 2019-06
• 期刊: Proceedings of the 2019 ACM SIGPLAN International Symposium on Memory Management
• 作者: Pengcheng Li;Hao Luo;C. Ding

Prediction and bounds on shared cache demand from memory access interleaving

• DOI: 10.1145/3210563.3210565
• 发表时间: 2018-06
• 期刊: Proceedings of the 2018 ACM SIGPLAN International Symposium on Memory Management
• 作者: Jacob Brock;C. Ding;Rahman Lavaee;Fangzhou Liu;Liang Yuan

LD: Low-Overhead GPU Race Detection Without Access Monitoring

LD：无需访问监控的低开销 GPU 竞争检测

• DOI: 10.1145/3046678
• 发表时间: 2017
• 期刊: ACM Transactions on Architecture and Code Optimization
• 影响因子: 1.6
• 作者: Li, Pengcheng;Hu, Xiaoyu;Chen, Dong;Brock, Jacob;Luo, Hao;Zhang, Eddy Z.;Ding, Chen
• 通讯作者: Ding, Chen

Locality analysis through static parallel sampling

• DOI: 10.1145/3192366.3192402
• 发表时间: 2018-06
• 期刊: Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation
• 作者: Dong Chen;Fangzhou Liu;C. Ding;Sreepathi Pai

Memory equalizer for lateral management of heterogeneous memory

• DOI: 10.1145/3132402.3132437
• 发表时间: 2017-10
• 期刊: Proceedings of the International Symposium on Memory Systems
• 作者: Chencheng Ye;Chen Ding;Hai Jin