Intelligent SSD: A turbo for big data mining
- Dept. of Electronics and Computer Engineering, Hanyang University
222, Wangsimni-ro, Seongdong-gu, Seoul, Republic of Korea
{dhbae,kjhfreedom,jyy0430}@agape.hanyang.ac.kr - Dept. of Computer and Software, Hanyang University
222, Wangsimni-ro, Seongdong-gu, Seoul, Republic of Korea
wook@hanyang.ac.kr - Dept. of Information Systems, Hanyang University
222, Wangsimni-ro, Seongdong-gu, Seoul, Republic of Korea
hoh@hanyang.ac.kr - S/W Development Team, Samsung Electronics Co., Ltd.
129, Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea
ci.park@samsung.com
Abstract
This paper introduces a new notion of the intelligent SSD and presents its potential benefits in terms of data mining applications. With intelligent SSDs, a large volume of data can be directly processed by CPU and DRAM inside intelligent SSDs, and the final result of a very small size needs to be transferred to the host CPU instead of all the data stored in intelligent SSDs. We first discuss design considerations of intelligent SSDs compared with the current SSD architecture. We then analyze the execution costs of data mining applications running on intelligent SSDs by formulating their cost models. Finally, we show the efficiency of performing data mining on intelligent SSDs by comparing it with those of traditional ones through a series of simulations. Through the experimental results, we show that the intelligent SSDs provide significant improvement in performance over the host CPUs in processing data-intensive data mining applications.
Key words
Intelligent SSD, big data mining, in-storage processing, active SSD
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS150820008D
Publication information
Volume 13, Issue 2 (June 2016)
Year of Publication: 2016
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
Available in PDF
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How to cite
Bae, D., Kim, J., Jo, Y., Kim, S., Oh, H., Park, C.: Intelligent SSD: A turbo for big data mining. Computer Science and Information Systems, Vol. 13, No. 2, 375–394. (2016), https://doi.org/10.2298/CSIS150820008D