CSR: Small: IOQL: an I/O Interface for Near-Data Processing

随着数据集的增长，在计算机中的不同单元之间移动数据的开销显著影响应用程序的执行时间。尽可能在数据源位置附近执行计算可以从根本上最大限度地减少数据移动的需求。然而，用现有的计算机系统很难做到这一点。该项目将开发一种输入/输出查询语言(IOQL)，为用户提供一种自然语言和界面，以方便用户描述要在数据存储附近计算的任务。IOQL基础设施优化和协调系统中所有任务的分配。IOQL将提升许多关键数据中心应用程序的性能。IOQL将通过提供一种易于理解的语法的查询语言、流行编程语言的应用编程接口、在运行时在不同计算资源之间优化和分配任务的查询引擎、与具有计算设施的数据存储/存储设备交互的内核模块以及IOQL的体系结构支持来实现其目标。IOQL将通过实施具有扩展数据存储设备和存储控制器的真实系统原型来评估建议的概念。该项目将衡量数据密集型计算内核、机器学习框架和数据库系统的性能、功率和能耗。IOQL将与现有计算系统，特别是数据中心兼容，以满足现实世界应用对金融(例如商业智能)、科学(例如模拟)和公共卫生(例如基因组学)应用的需求。IOQL的设计将支持拟议任务的额外成本降至最低，但最大限度地提高了框架的可扩展性，使IOQL成为解决类似问题的理想长期解决方案。该项目将在相关的同行评议会议、期刊和研讨会上发布研究成果，允许公众访问。这个项目还将使IOQL的稳定源代码和所有相关模块在https://github.com/ESCALNCSU.上可用该研究小组计划将所有相关数据保留至少三年。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

As data sets grow, the overhead of moving data around different units in computers significantly affect the execution time of applications. Performing computation near the source location of data as much as possible can fundamentally minimize the demand of data movement. However, doing this is difficult with the existing computer systems. This project will develop a Input/Output Query Language (IOQL) to provide a natural language and interface for users to easily describe the tasks to compute near data storage. The IOQL infrastructure optimizes and coordinates the allocation of all tasks in the system. IOQL will boost the performance of many critical datacenter applications. IOQL will achieve its goal through presenting a query language with easy-to-understand syntax, application programming interfaces for popular programming languages, a query engine that optimizes and assigns tasks among heterogeneous computing resources during runtime, kernel modules to interact with data storage/memory devices with computing facilities and architectural support of IOQL. IOQL will evaluate the proposed concepts by implementing real system prototype with extended data storage devices and memory controllers. This project will measure the performance, power and energy consumption for data-intensive compute kernels, machine learning frameworks and database systems.IOQL will be made compatible with existing computing systems, especially data centers, to address the demand of real-world applications for financial (e.g., business intelligence), scientific (e.g., simulation), and public health (e.g., genomics) applications. The design of IOQL minimizes the amount of additional costs of supporting the proposed tasks but maximizes extensibility of the framework, making IOQL an ideal long-term solution for similar problems.This project will publish research results in related peer-reviewed conferences, journals and workshops that allow public to access. This project will also make the stabilized source code of IOQL and all related modules available on https://github.com/ESCALNCSU. The research group plans to retain all related data for at least three years.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.