在选定的机器学习算法中增强数据局部性的指南
Guidelines for enhancing data locality in selected machine learning algorithms
摘要：
为了应对更大更复杂的新一代数据的复杂性，机器学习（ML）技术可能是首先使用的。为了让ML算法在合理的时间内产生结果，它们需要有效地实现。本文分析了提高机器学习算法性能的一种方法，即利用数据局部性。由于使用某些硬件技术来提高性能，数据局部性和访问模式通常是计算系统中性能问题的核心。改变访问模式以增加局部性可以显著提高给定算法的性能。此外，重复的数据访问可以被视为数据移动中的冗余。类似地，在计算的重复中也可能存在冗余。这项工作还确定了一些机会，避免这些冗余直接重用计算结果。我们首先激励为什么和如何更有效的实现可以实现利用重用的存储层次结构的现代指令集处理器。接下来，我们记录在一些选定的机器学习算法中这种重用的可能性。
英文摘要：
To deal with the complexity of the new bigger and more complex generation of data, machine learning (ML) techniques are probably the first and foremost used. For ML algorithms to produce results in a reasonable amount of time, they need to be implemented efficiently. In this paper, we analyze one of the means to increase the performances of machine learning algorithms which is exploiting data locality. Data locality and access patterns are often at the heart of performance issues in computing systems due to the use of certain hardware techniques to improve performance. Altering the access patterns to increase locality can dramatically increase performance of a given algorithm. Besides, repeated data access can be seen as redundancy in data movement. Similarly, there can also be redundancy in the repetition of calculations. This work also identifies some of the opportunities for avoiding these redundancies by directly reusing computation results. We start by motivating why and how a more efficient implementation can be achieved by exploiting reuse in the memory hierarchy of modern instruction set processors. Next we document the possibilities of such reuse in some selected machine learning algorithms.
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