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基于带状图的图优化及其在图书馆性能调优中的应用

Bandit-Based Optimization on Graphs with Application to Library Performance Tuning
课程网址: http://videolectures.net/icml09_rimmel_bbogwalpt/  
主讲教师: Arpad Rimmel
开课单位: 国家科学研究中心
开课时间: 2009-08-26
课程语种: 英语
中文简介:
为一类递归算法(例如快速傅里叶变换)选择快速实现的问题可以被表达为由适当定义的语法生成的语言的优化问题。我们提出了一种新算法,通过将其减少到最大化有向无环图的汇点上的目标函数来解决该问题。该算法使用蒙特卡罗评估节点,并通过考虑局部最大k武装强盗在最有希望的方向上生成子图。当在自适应线性变换库中使用时,与现有算法相比,它将搜索时间减少了一个数量级。在某些情况下,所发现的实现的性能也增加了高达10%,这具有相当大的实际重要性,因为它因此改善了使用该库的所有应用程序的性能。
课程简介: The problem of choosing fast implementations for a class of recursive algorithms such as the fast Fourier transforms can be formulated as an optimization problem over the language generated by a suitably defined grammar. We propose a novel algorithm that solves this problem by reducing it to maximizing an objective function over the sinks of a directed acyclic graph. This algorithm valuates nodes using Monte-Carlo and grows a subgraph in the most promising directions by considering local maximum k-armed bandits. When used inside an adaptive linear transform library, it cuts down the search time by an order of magnitude compared to the existing algorithm. In some cases, the performance of the implementations found is also increased by up to 10% which is of considerable practical importance since it consequently improves the performance of all applications using the library.
关 键 词: 递归算法; 目标函数; 蒙特卡罗评估节点
课程来源: 视频讲座网
最后编审: 2019-04-24:lxf
阅读次数: 46