摘要(英) |
Due to the rapid growth of computing data, DRAM-based main memory cannot accommodate all to-be-processed data from data-intensive applications (e.g., machine learning algorithms and recommendation systems). Therefore, data movement between main memory and a storage device results in a significant performance issue. When a traditional NAND-based solid-state drive (SSD) is applied to a computer architecture, the performance issue cannot be tackled because a storage drive cannot distinguish the types of data from the host system. However, a new type storage medium, namely open-channel SSD (OCSSD), has been proposed to provide a path to optimize data placement on the storage space from the host-side system. In this study, we develop a new data access model for a well-known data-intensive application (i.e., deep learning recommendation system (DLRM)) on an OCSSD storage drive. Our solution, called OC-DLRM, can minimize the I/O traffic to the flash memory storage device by considering the I/O unit of a flash memory drive to place the frequently-accessed data together. According to our experimental results, the OC-DLRM significantly decrease the amount of I/O traffic between memory and storage devices, compared with the traditional virtual memory management solution. |
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