https://scholars.lib.ntu.edu.tw/handle/123456789/581289
標題: | Analyzing the Interplay between Random Shuffling and Storage Devices for Efficient Machine Learning | 作者: | Ke Z.-L Cheng H.-Y Yang C.-L Huang H.-W. CHIA-LIN YANG |
關鍵字: | Deep learning; Deep neural networks; Hard disk storage; Learning systems; Random access storage; Support vector machines; Turing machines; Virtual storage; Convergence rates; Hard disk drives; Performance analysis; Random access; Random storage; Solid state drives (SSD); Testing accuracy; Training time; Learning algorithms | 公開日期: | 2021 | 起(迄)頁: | 276-287 | 來源出版物: | Proceedings - 2021 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2021 | 摘要: | Machine learning algorithms, such as Support Vector Machine (SVM) and Deep Neural Network (DNN), have gained a lot of interest recently. When training a machine learning algorithm, randomly shuffling all the training data can improve the testing accuracy and boost the convergence rate. Nevertheless, realizing training data random shuffling in a real system is not straightforward due to the slow random accesses in hard disk drives (HDDs). Common random shuffling implementations assume that HDD is used as storage, so they sacrifice the random degree of shuffling to reduce random storage accesses. Different from conventional HDD, emerging solid-state drive (SSD) based storage devices, such as Intel Optane SSD, offer fast random accesses. In this paper, we explore the opportunities to take advantage of the fast random access property in SSD to perform full-range random shuffling without taking up precious CPU memory and study the interplay between different shuffling methods and various types of storage devices. We use a lightweight implementation of random shuffling (LIRS) as an example of the SSD-aware shuffling method to conduct performance analysis. Evaluations show that, compared to conventional shuffling methods, LIRS can improve convergence rate and reduce the total training time of SVM and DNN by 67.1% and 33.9% on average. ? 2021 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105396926&doi=10.1109%2fISPASS51385.2021.00050&partnerID=40&md5=da4d94ba379b228d0e391f69b4b69b68 https://scholars.lib.ntu.edu.tw/handle/123456789/581289 |
DOI: | 10.1109/ISPASS51385.2021.00050 |
顯示於: | 資訊工程學系 |
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