SHF:Small: Optimization of Parallel and Shared Cache Memory using the Footprint Theory

SHF:Small：使用足迹理论优化并行和共享缓存内存

• 批准号: 1717877
• 负责人: Chen Ding
• 金额: $ 45.63万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717877&HistoricalAwards=false
• 关键词: SHF; Small; Optimization; Parallel; Shared

Multicore processors bring a tremendous increase in computing power to personal, scientific, and business computing platforms. Most on-chip memory in these processors is devoted to shared cache, making it a major factor in performance. A common practice is to improve cache performance by building a path of improvements through testing. However, testing may take too many steps, it may not converge to a stable solution, and most importantly for large scale parallel programs, the solution is far from optimal since testing covers only a minuscule fraction of all possibilities. Instead of testing, this research provides software developers and hardware engineers new tools based on modeling. The research builds on the past NSF supported research which has developed the footprint theory for sequential applications. This work solves the new problems of data sharing and extends the locality theory to optimize the parallel code. Parallel systems require complex models, and this complexity is addressed by composable models to solve large scale problems with large scale modeling. The new tools include statistical models of both program and machine characteristics. Program models include profiling analysis that derive the data and cache sharing in all thread combinations and data placements, in cache of all sizes. Cache models analyze the effect of many hardware designs including associativity, exclusivity, coherence, cache-way partitioning, and transient loads/stores. Combined use of these models enables parallel program optimization and improves thread and data placement.

多核处理器为个人、科学和商业计算平台带来了计算能力的巨大增长。这些处理器中的大多数片上内存都用于共享缓存，使其成为性能的主要因素。 一种常见的做法是通过测试构建改进路径来提高缓存性能。 然而，测试可能需要太多的步骤，它可能不会收敛到一个稳定的解决方案，最重要的是，对于大规模的并行程序，解决方案是远远不是最佳的，因为测试只涵盖了所有可能性的一小部分。 这项研究为软件开发人员和硬件工程师提供了基于建模的新工具，而不是测试。 该研究建立在美国国家科学基金会过去支持的研究的基础上，该研究开发了顺序应用的足迹理论。该工作解决了数据共享的新问题，并扩展了局部性理论，以优化并行代码。并行系统需要复杂的模型，这种复杂性是由可组合的模型来解决大规模建模的大规模问题。新工具包括程序和机器特性的统计模型。 程序模型包括剖析分析，该剖析分析在所有线程组合和数据放置中导出数据和高速缓存共享，在所有大小的高速缓存中。高速缓存模型分析了许多硬件设计的影响，包括关联性，排他性，一致性，高速缓存方式分区和瞬态加载/存储。这些模型的组合使用可以实现并行程序优化并改进线程和数据放置。

项目成果

CLAM: Compiler Lease of Cache Memory

• DOI: 10.1145/3422575.3422800
• 发表时间: 2020-09
• 期刊: Proceedings of the International Symposium on Memory Systems
• 作者: Ian Prechtl;Ben Reber;C. Ding;D. Patru;Dong Chen

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

Beating OPT with Statistical Clairvoyance and Variable Size Caching

• DOI: 10.1145/3297858.3304067
• 发表时间: 2019-04
• 期刊: Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems
• 作者: Pengcheng Li;Colin Pronovost;William D. Wilson;B. Tait;Jie Zhou;C. Ding;J. Criswell

PAYJIT: space-optimal JIT compilation and its practical implementation

PAYJIT：空间优化的JIT编译及其实际实现

• DOI: 10.1145/3178372.3179523
• 发表时间: 2018
• 期刊: CC 2018
• 作者: Brock, Jacob;Ding, Chen;Xu, Xiaoran;Zhang, Yan
• 通讯作者: Zhang, Yan

Fast Miss Ratio Curve Modeling for Storage Cache

• DOI: 10.1145/3185751
• 发表时间: 2018-04
• 期刊: ACM Transactions on Storage (TOS)
• 作者: Xiameng Hu;Xiaolin Wang;Lan Zhou;Yingwei Luo;Zhenlin Wang;C. Ding;Chencheng Ye