March 2, 2023, 10:53 p.m. | USENIX

USENIX www.youtube.com

SHADE: Enable Fundamental Cacheability for Distributed Deep Learning Training

Redwan Ibne Seraj Khan and Ahmad Hossein Yazdani, Virginia Tech; Yuqi Fu, University of Virginia; Arnab K. Paul, BITS Pilani; Bo Ji and Xun Jian, Virginia Tech; Yue Cheng, University of Virginia; Ali R. Butt, Virginia Tech

Deep learning training (DLT) applications exhibit unique I/O workload behaviors that pose new challenges for storage system design. DLT is I/O intensive since data samples need to be fetched continuously from a remote storage. …

applications bits challenges deep learning design distributed dlt enable fast paul shade storage system tech training university virginia workload

Information Security Engineers

@ D. E. Shaw Research | New York City

Technology Security Analyst

@ Halton Region | Oakville, Ontario, Canada

Senior Cyber Security Analyst

@ Valley Water | San Jose, CA

Senior Security Analyst

@ Oracle | United States

Associate Vulnerability Management Specialist

@ Diebold Nixdorf | Hyderabad, Telangana, India

Cybersecurity Architect, Infrastructure & Technical Security

@ KCB Group | Kenya