||-rosetta
||-rosetta
复制标题
||-罗塞塔
DOI：
10.1007/978-3-662-62798-3_2
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
J. Komorowski
中科院分区：
文献类型：
--
作者：
N. Baltzer;J. Komorowski
技术每天都在进步。为了使既定理论保持相关性，必须更新此类理论的实现以利用新的改进。ROSETTA是1994年提出的一个基于粗糙集理论的机器学习框架，旨在将粗糙集理论应用于机器学习领域。从那时起，在计算机技术领域发生了很多事情，为了充分利用这些好处，ROSETTA需要发展。我们在ROSETTA中设计并实现了一个多核执行过程，针对速度和模块化扩展进行了优化。该程序使用四个不同大小的数据集进行了测试，以测试计算速度和内存使用情况，这些因素考虑了分类和机器学习的主要限制。结果表明，计算速度的增加与预期的增益一致。在五个线程之后，每个线程的内存使用比例小于线性，内存的增加主要基于数据集中对象的数量。数据中的特征数量增加了所需的基本内存，但不会显著影响线程的内存扩展。多核实施取得了成功，||-ROSETTA（发音为ROSEL-ROSETTA）能够充分利用现代硬件解决方案。
Technology improves every day. In order for an established theory to maintain relevance, implementations of such theory must be updated to take advantage of the new improvements. ROSETTA, a framework based on Rough Set theory, was developed in 1994 to exploit Rough Set paradigms in Machine Learning. Since then, much has happened in the field of Computer Technology, and to fully exploit these benefits ROSETTA needed to evolve. We designed and implemented a multi-core execution process in ROSETTA, optimized for speed and modular extension. The program was tested using four datasets of different sizes for computational speed and memory usage, the factors considered the primary limitations of classification and Machine Learning. The results show an increase in computation speed consistent with expected gains. The scaling per thread of memory usage was less than linear after five threads with increases in memory based primarily on the number of objects in the dataset. The number of features in the data increased the base memory needed but did not significantly impact the memory scaling by threads. The multi-core implementation was successful, and ||-ROSETTA (pronounced Parallel-ROSETTA) is capable of fully exploiting modern hardware solutions.