Parallel and Distributed Machine Learning - Smart Data Analysis in the Multicore Era

在大型数据中心，我们的研究将通过使用显卡来降低能源消耗，显卡的计算功率比比传统处理器要好得多。在台式计算机上，它将通过启用高效的垃圾邮件过滤和内容分析算法，使机器学习变得实用。在联网系统上，它将导致分布式推理、缓存和协作过滤应用程序，这些应用程序将减少所需的带宽，并使互联网对用户更安全。最后，它将使传感器网络能够快速部署用于监测和检测，例如用于环境监测和保卫澳大利亚边境。

In large data centres our research will lead to reduced energy consumption by using graphics cards which have a much better computation to power ratio than traditional processors. On desktop computers, it will make machine learning practical by enabling efficient algorithms for spam filtering and content analysis. On networked systems it will lead to distributed inference, caching and collaborative filtering applications which will both reduced the bandwidth required and make the internet safer for users. Finally, it will enable rapid deployment of sensor networks for monitoring and detection, such as for environmental monitoring and safeguarding Australia's borders.