III: Small: Enabling the Best Utilization of GPUs for In-Memory Data Management Systems

III：小型：为内存数据管理系统实现 GPU 的最佳利用

• 批准号: 1718450
• 负责人: Xiaodong Zhang
• 金额: $ 50万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718450&HistoricalAwards=false
• 关键词: III; Small; Enabling; Best; Utilization

The continuously increasing demand for fast and real-time data analytics requires that in-memory database systems provide both high-performance transaction processing and low-latency query response time. To accomplish these two goals is a highly challenging task as multiple workloads are co-run on conventional CPU/multicores due to different workload characteristics and different quality of service requirements. The rise of general-purpose graphical processing units (GPU) brings a decoupling opportunity of maximizing query execution performance while minimizing interference to transaction processing. However, best utilization of such a hardware device in database systems must effectively address several mismatches between the parallel computing-oriented architecture of GPU and the data processing-oriented database query workloads. This project seeks a solution of building a GPU-based query execution engine and offers in-memory database systems as an unprecedented opportunity and capability to execute both transaction workloads and analytics workloads in a high-performance and low-cost way. This project attempts to make a high broader impact by transforming basic research results into practical database systems, and by training both undergraduate and graduate students with research activities, and by timely introducing new research results into classrooms.Specifically, the project will address three mismatching issues, including (1) the one between high parallel computing power in GPU and slow data transfer speeds from/to main memory; (2) the one between GPU's limited programming capabilities and database query's complex structures; and (3) the one between the availability of massive parallel processing cores and the lack of system software support for concurrent task executions and memory management. This project will apply a holistic system design methodology to cohesively address these challenges by carrying out several closely related research tasks. (1) The project team will develop a software translation framework with multi-level abstractions and optimizations to automatically translate Structured Query Language queries into highly-efficient programs running on GPUs. (2) The team will develop query execution cost models by considering GPU performance factors to support query optimization and dynamic re-optimization. (3) The team will design specific algorithms and query execution techniques to efficiently handle two complex but practically important queries, namely nested sub-queries and recursive queries. (4) The team will build a software layer for system-level device memory management and dynamic query scheduling. (5) A final task will be to integrate the project outcome into representative open source in-memory systems with comprehensive workloads. The research efforts in this project will be open source.

对快速和实时数据分析的需求不断增加，要求内存数据库系统同时提供高性能交易处理和低延迟查询响应时间。实现这两个目标是一项极具挑战性的任务，因为由于不同的工作负载特征和服务质量不同，多个工作负载是在常规CPU/Multicores上共同运行的。通用图形处理单元（GPU）的兴起带来了最大化查询执行性能的脱钩机会，同时最大程度地减少了对交易处理的干扰。但是，数据库系统中这种硬件设备的最佳利用必须有效地解决GPU的并行计算架构与以数据处理为导向的数据库查询工作负载之间的几个不匹配。该项目寻求一种解决基于GPU的查询执行引擎的解决方案，并提供内存数据库系统，作为前所未有的机会和能力，以高性能和低成本方式执行交易工作负载和分析工作负载。 This project attempts to make a high broader impact by transforming basic research results into practical database systems, and by training both undergraduate and graduate students with research activities, and by timely introducing new research results into classrooms.Specifically, the project will address three mismatching issues, including (1) the one between high parallel computing power in GPU and slow data transfer speeds from/to main memory; （2）GPU有限的编程功能和数据库查询的复杂结构之间的一个； （3）大规模并行处理核心的可用性与缺乏同时执行任务执行和内存管理的系统软件支持之间。该项目将通过执行几项密切相关的研究任务，应用整体系统设计方法来凝聚力解决这些挑战。 （1）项目团队将开发一个具有多级抽象和优化的软件翻译框架，以自动将结构化查询语言查询转换为在GPU上运行的高效程序。 （2）团队将通过考虑支持查询优化和动态重优化的GPU性能因素来开发查询执行成本模型。 （3）团队将设计特定的算法和查询执行技术，以有效处理两个复杂但实际上重要的查询，即嵌套的子查询和递归查询。 （4）团队将构建一个软件层，用于系统级设备内存管理和动态查询计划。 （5）最后一项任务是将项目结果与全面的工作负载相结合到代表性开源内存中。该项目的研究工作将是开源的。

项目成果

Automating Incremental and Asynchronous Evaluation for Recursive Aggregate Data Processing

• DOI: 10.1145/3318464.3389712
• 发表时间: 2020-05
• 期刊: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
• 作者: Qiange Wang;Yanfeng Zhang;Hao Wang;Liang Geng;Rubao Lee;Xiaodong Zhang;Ge Yu

Catfish: Adaptive RDMA-enabled R-Tree for Low Latency and High Throughput

• DOI: 10.1109/icdcs.2019.00025
• 发表时间: 2019-07
• 期刊: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)
• 作者: Mengbai Xiao;Hao Wang;Liang Geng;Rubao Lee;Xiaodong Zhang

NeutronStar: Distributed GNN Training with Hybrid Dependency Management

• DOI: 10.1145/3514221.3526134
• 发表时间: 2022-06
• 期刊: Proceedings of the 2022 International Conference on Management of Data
• 作者: Qiange Wang;Yanfeng Zhang;Hao Wang;Chao Chen;Xiaodong Zhang;Geoffrey X. Yu

HYPHA: a framework based on separation of parallelism to accelerate persistent homology matrix reduction

HYPHA：基于并行分离加速持久同源矩阵约简的框架

• 发表时间: 2019
• 期刊: Proceedings of 33rd ACM International Conference on Supercomputing (ICS 2019
• 作者: Simon Zhang, Mengbai Xiao

Mixer: Efficiently Understanding and Retrieving Visual Content at Web-Scale

• DOI: 10.14778/3476311.3476371
• 发表时间: 2021-07
• 期刊: Proc. VLDB Endow.
• 作者: Mengbai Xiao;An Qin;Yongwei Wu;Xinjie Huang;Xiaodong Zhang