High-Performance Network Data Transfers to GPU : A Study of Nvidia GPU Direct RDMA and GPUNetIO

Sammanfattning: This study investigates high-performance network data transfers, focusing on Nvidia Graphics Processing Unit (GPU) Direct Remote Direct Memory Access (RDMA) and GPUNetIO. These methods have emerged as promising strategies for improving data communication between GPUs and network interfaces, but harnessing their potential requires meticulous configuration and optimization. This research aims to clarify those architectures and achieve optimal performance in this context. The study begins with analyzing the source code for both architectures, explaining their underlying principles and what they have improved on the previous structures. A useroriented testing tool is also developed to provide users with a simplified interface for conducting tests and system configuration requirements. The research methodology consists of reviewing the literature and analyzing the source code of GPUDirect RDMA and GPUNetIO. Additionally, experiments are designed to evaluate various performance aspects, ranging from Central Processing Unit (CPU)- related factors to GPU metrics and network card performance. The results indicate a significant acceleration in data copying when based on GPUDirect RDMA technology. The introduction of GPUNetIO leads to a substantial decrease in CPU utilization. Furthermore, the user interface is designed for simple deployment on hosts and easy access by users. The interface is equipped with the recommended configuration settings.