@inproceedings{b079b6474f3840579a22a2e5585b265c,
title = "OC-DLRM: Minimizing the I/O Traffic of DLRM Between Main Memory and OCSSD",
abstract = "Due to the exponential growth of data in computing, DRAM-based main memory is now insufficient for data-intensive applications like machine learning and recommendation systems. This has led to a performance issue involving data transfer between main memory and storage devices. Conventional NAND-based SSDs are unable to efficiently handle this problem as they can't distinguish between data types from the host system. In contrast, open-channel SSDs (OCSSD) offer a solution by optimizing data placement from the host-side system. This research focuses on developing a new data access model for deep learning recommendation systems (DLRM) using OCSSD storage drives, called OC-DLRM. OC-DLRM reduces I/O traffic to flash memory by aggregating frequently-accessed data using the I/O unit of a flash memory drive. Our experiments show that OC-DLRM has significant performance improvement compared with traditional swapping space management techniques.",
keywords = "deep learning, flash memory, Open channel SSD, recommendation model, virtual memory",
author = "Ti, {Shang Hung} and Chen, {Tseng Yi} and Yeh, {Tsung Tai} and Chen, {Shuo Han} and Liang, {Yu Pei}",
note = "Publisher Copyright: {\textcopyright} 2024 EDAA.; 2024 Design, Automation and Test in Europe Conference and Exhibition, DATE 2024 ; Conference date: 25-03-2024 Through 27-03-2024",
year = "2024",
language = "???core.languages.en_GB???",
series = "Proceedings -Design, Automation and Test in Europe, DATE",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 Design, Automation and Test in Europe Conference and Exhibition, DATE 2024 - Proceedings",
}