Events

National Center for Supercomputing Applications master calendar

View Full Calendar

NCSA staff who would like to submit an item for the calendar can email newsdesk@ncsa.illinois.edu.

Haoyang Zhang "G10: Enabling An Efficient Unified GPU Memory and Storage Architecture with Smart Tensor Migrations"

Event Type
Lecture
Sponsor
Department of Computer Science
Location
Thomas M. Siebel Center for Computer Science SC 2405
Date
Oct 17, 2023   4:00 pm  
Speaker
Haoyang Zhang, PhD Student, The University of Illinois Champaign-Urbana
Contact
Kalen Mc Gowan
E-Mail
kalenmcg@illinois.edu
Phone
217-333-2383
Views
62
Originating Calendar
Computer Science Speakers Calendar

Abstract:

To break the GPU memory wall for scaling deep learning workloads, a variety of architecture and system techniques have been proposed recently. Their typical approaches include memory extension with flash memory and direct storage access. However, these techniques still suffer from suboptimal performance and introduce complexity to the GPU memory management, making them hard to meet the scalability requirement of deep learning workloads today. We present a unified GPU memory and storage architecture named G10 driven by the fact that the tensor behaviors of deep learning workloads are highly predictable. G10 integrates the host memory, GPU memory, and flash memory into a unified memory space, to scale the GPU memory capacity while enabling transparent data migrations. Based on this unified GPU memory and storage architecture, G10 utilizes compiler techniques to characterize the tensor behaviors in deep learning workloads. Therefore, it can schedule data migrations in advance by considering the available bandwidth of flash memory and host memory. The cooperative mechanism between deep learning compilers and the unified memory architecture enables G10 to hide data transfer overheads in a transparent manner. We implement G10 based on an open-source GPU simulator. Our experiments demonstrate that G10 outperforms state-of-the-art GPU memory solutions by up to 1.75 ×, without code modifications to deep learning workloads. With the smart data migration mechanism, G10 can reach 90.3% of the performance of the ideal case assuming unlimited GPU memory.

Bio

Haoyang Zhang is a second-year CS Ph.D. student at the University of Illinois Urbana-Champaign (UIUC), advised by Prof. Jian Huang. Haoyang's current research focuses on exploiting HW-SW co-design and building novel memory/storage systems to support AI infrastructure/platforms.

link for robots only