Presentation
A Zero-Copy Storage with Metadata-Driven File Management Using Persistent Memory
DescriptionPersistent Memory (PM) is a promising next-generation storage device, combining features of both volatile memory (like DRAM) and non-volatile memory (like SSDs). Many studies use PM to optimize training to advance deep learning technology. However, these studies have not addressed the issue of multiple copies of training data during deep learning, leading to reduced training efficiency. In this study, we first analyze the characteristics of PM and mainstream file systems. We then explore PM's byte addressability to manage metadata and data efficiently. This approach minimizes multiple I/O operations of tasks involving repeated read-write data accesses, such as machine learning datasets, enabling zero-copy data handling and significant speedups of read-and-write operations.
Event Type
Posters
TimeThursday, 21 November 202410am - 5pm EST
LocationB302-B305