OC-DLRM: Minimizing the I/O Traffic of DLRM Between Main Memory and OCSSD
Shang-Hung Ti, Tseng‐Yi Chen, Tsung Tai Yeh, Shuo-Han Chen, Yu-Pei Liang · 2024
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.