FPGA acceleration of large-scale database searches

大规模数据库搜索的 FPGA 加速

• 批准号: 488914-2015
• 负责人: Chow, Paul
• 金额: $ 1.82万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=586413
• 关键词: FPGA; acceleration; large; scale; database

The world of computing is changing as the amount of data being processed is growing rapidly and the energy required to do the processing becomes a limitation. At the same time, there is a constant desire for increased performance. Technological limitations make it difficult to continue only with the tried and true processing model using standard microprocessors. To make the required improvements, the computing world has started to use more specialized processors. Graphics processing units (GPUs) are the most common computing unit used after microprocessors. GPUs are particularly effective in applications that have very structured computations. More recently, it has been shown that field-programmable gate arrays (FPGAs), a programmable hardware chip technology, can also be used effectively for some types of computations. This project explores the use of FPGAs in one specific application area. In today's world of big data, there is a need to search extremely large databases. These searches are run using large data centres, where power and performance are especially important in terms of operation costs and the responsiveness to the customer. FPGAs have been shown to be effective in small-scale database systems. This project will integrate FPGAs into a large-scale database application and quantify the benefits of FPGAs for very large database searches.

随着处理的数据量快速增长，计算世界正在发生变化，而处理所需的能量变得有限。与此同时，人们一直渴望提高性能。技术限制使得使用标准微处理器的经过验证的真实处理模型很难继续下去。为了实现所需的改进，计算机世界已经开始使用更专业的处理器。图形处理单元（gpu）是继微处理器之后最常用的计算单元。gpu在具有非常结构化计算的应用程序中特别有效。最近，已经证明现场可编程门阵列（fpga），一种