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Parallel approaches to machine learning-A comprehensive survey

机译:机器学习的并行方法-综合调查

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Literature has always witnessed efforts that make use of parallel algorithms / parallel architecture to improve performance; machine learning space is no exception. In fact, a considerable effort has gone into this area in the past fifteen years. Our report attempts to bring together and consolidate such attempts. It tracks the development in this area since the inception of the idea in 1995, identifies different phases during the time period 1995-2011 and marks important achievements. When it comes to performance enhancement, GPU platforms have carved a special niche for themselves. The strength of these platforms comes from the capability to speed up computations exponentially by way of parallel architecture / programming methods. While it is evident that computationally complex processes like image processing, gaming etc. stand to gain much from parallel architectures; studies suggest that general purpose tasks such as machine learning, graph traversal, and finite state machines are also identified as the parallel applications of the future. Map reduce is another important technique that has evolved during this period and as the literature has it, it has been proved to be an important aid in delivering performance of machine learning algorithms on CPUs. The report summarily presents the path of developments.
机译:文献一直见证了利用并行算法/并行体系结构来提高性能的努力。机器学习空间也不例外。实际上,在过去的15年中,已经在该领域做出了相当大的努力。我们的报告试图汇集和巩固这种尝试。它追踪自1995年提出该想法以来该领域的发展,确定了1995-2011年期间的不同阶段,并标志着重要的成就。在性能增强方面,GPU平台为自己创造了一个特殊的市场。这些平台的优势来自通过并行体系结构/编程方法以指数方式加速计算的能力。显而易见的是,诸如并行处理的架构会从图像处理,游戏等计算复杂的过程中受益匪浅。研究表明,诸如机器学习,图形遍历和有限状态机之类的通用任务也被认为是未来的并行应用。映射缩减是在此期间发展的另一项重要技术,正如文献所证明的那样,它已被证明是在CPU上提供机器学习算法性能的重要帮助。该报告简要介绍了发展道路。

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