Year: 2025
Type: conference-paper
Abstract: Graphics Processing Units (GPUs) excel at high-performance computing tasks, including multimedia rendering, cryptomining, deep learning, and natural language processing, due to their massive parallelism and high memory bandwidth.However, the growing size of models and datasets in these domains increasingly exceeds the memory capacity of a single GPU, resulting in significant performance overheads.To mitigate this issue, developers are often forced to partition data and manually manage transfers between GPU and host memory-a labor-intensive approach that becomes impractical for workloads with irregular memory access patterns, such as deep learning, recommendation systems, and graph processing.Programming abstractions like Unified Virtual Memory (UVM) simplify development by offering a unified memory space across the system and handling data transfers automatically.Unfortunately, UVM introduces substantial overhead due to frequent OS involvement and inefficient data movement, particularly when GPU memory is oversubscribed.This paper presents DREAM, a GPU memory management system that leverages an RDMA-capable network device to implement a programmer-agnostic lightweight virtual memory system, eliminating CPU/OS involvement.DREAM supports on-demand page migration for GPU applications by delegating memory management and page migration tasks to GPU threads.Since current CPU architectures do not support GPU-initiated memory management, DREAM uses a network interface card to enable efficient, transparent page migration.By offloading memory management to the GPU, DREAM achieves up to 4× higher performance than UVM
Institution: University of California, Riverside
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: National Science Foundation
Awards: CAREER: Enabling Memory-Centric Computing from Internet of Things to Cloud, FuSe-TG: Domain-Specific 3D ReRAM-based Processing-in-Memory Accelerators for Streaming Time Series Applications, Collaborative Research: SHF: Medium: Approximate Computing for Machine Learning Security: Foundations and Accelerator Design, CNS Core: Medium: Real-time Energy-elastic GPUs for Embedded Autonomous Systems
OpenAlex ID: W4413411852