WOW: what the world of (data) warehousing can learn from the World of Warcraft

René Müller, T. Kaldewey, G. Lohman, J. McPherson
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引用次数: 1

Abstract

Although originally designed to accelerate pixel monsters, graphics Processing Units (GPUs) have been used for some time as accelerators for selected data base operations. However, to the best of our knowledge, no one has yet reported building a complete system that allows executing complex analytics queries, much less an entire data warehouse benchmark at realistic scale. In this demo, we showcase such a complete system prototype running on a high-end GPU paired with an IBM storage system that achieves >90% hardware efficiency. Our solution delivers sustainable high throughput for business analytics queries in a realistic scenario, i.e., the Star Schema Benchmark at scale factor 1,000. Attendees can interact with our system through a graphical user interface on a tablet PC. They will be able to experience first hand how queries that require processing more than six billion rows, or 100 GB of data, are answered in less than 20 seconds. The user interface allows submitting queries, live performance monitoring of the current query all the way down to the operator level, and viewing the result once the query completes.
《魔兽世界》:数据仓库领域可以从《魔兽世界》中学到什么
虽然最初设计图形处理单元(gpu)是为了加速像素怪物,但图形处理单元(gpu)已经被用作选定数据库操作的加速器有一段时间了。然而,据我们所知,还没有人报告构建了一个允许执行复杂分析查询的完整系统,更不用说实际规模的整个数据仓库基准了。在这个演示中,我们展示了这样一个完整的系统原型,它运行在一个高端GPU上,搭配一个IBM存储系统,实现了90%的硬件效率。我们的解决方案为现实场景中的业务分析查询提供了可持续的高吞吐量,例如,规模因子为1000的星型模式基准。与会者可以通过平板电脑上的图形用户界面与我们的系统进行交互。他们将能够亲身体验需要处理超过60亿行或100 GB数据的查询如何在不到20秒的时间内得到回答。用户界面允许提交查询,对当前查询进行实时性能监控,一直到操作符级别,并在查询完成后查看结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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