I-Corps: Graphics Processing Unit-Based Data Management System Software

I-Corps项目更广泛的影响/商业潜力在于该技术可以为许多行业带来更高的数据库生产力。基于传统的基于拉取的数据库引擎设计和CPU硬件的现有软件系统在大规模数据的管理和分析中通常不提供期望的高吞吐量和/或短响应时间。这里开发的技术提供了一个更有效的解决方案来应对这些挑战，其商业化将可能影响包括医疗保健，零售和在线广告在内的大量行业的数据管理和分析实践。I-Corps项目旨在探索数据管理系统的商业潜力，该系统具有新颖的软件架构和先进的数据分析功能，并构建在图形处理单元（GPU）等大规模并行硬件上。该技术采用数据流（基于推送）设计下的数据处理引擎，并实现了GPU上大量计算资源的极高利用率。目前的实验表明，数据处理吞吐量和延迟比为相同目的而构建的最佳已知系统好一个数量级。 该技术背后的研究补充了数据库系统和并行计算领域的当前工作。面向系统的方法与现有的GPU工作形成鲜明对比，后者通常专注于解决单个问题，因此具有高度的创造性。创新也出现在用于满足系统设计挑战的正式方法中，例如GPU中的动态资源分配。

The broader impact/commercial potential of this I-Corps project lies in the improved database productivity that the technology can bring to many industries. Existing software systems based on traditional pull-based database engine design and CPU hardware often do not provide the desired high throughput and/or short response time in the management and analysis of large-scale data. The technology developed here offers a more efficient solution to meet such challenges and its commercialization will potentially impact the practice of data management and analysis in a large number of industries including healthcare, retailing, and online advertising. Successful deployment of these the systems developed here will enable a range of industry sectors to efficiently harness big data analytics.This I-Corps project explores the commercial potential of a data management system with a novel software architecture and advanced data analytics functionalities built on massively parallel hardware such as Graphics Processing Units (GPUs). The technology features a data processing engine under a data streaming (push-based) design and achieves very high utilization of a multitude of computing resources on GPUs. Current experiments show that the data processing throughput and latency are about one order of magnitude better than best known systems built for the same purposes. The research behind this technology complements current work in database systems and parallel computing fields. The system-oriented approach is in sharp contrast to existing GPU work that typically focuses on solving individual problems and thus highly creative. Innovations are also seen in the formal methods used for meeting system design challenges such as dynamic resource allocation in GPUs.