Analyzing GPU Performance in Virtualized Environments: A~Case Study

2026-08-03

AI Summary

This work addresses the critical gap in existing performance analysis tools, which largely lack support for virtual GPUs (vGPUs) and thus struggle to diagnose performance bottlenecks in vGPU-accelerated applications. To tackle this limitation, the authors design and implement a novel vGPU performance profiling tool tailored for Intel GVT-g. The tool introduces, for the first time, a fine-grained software tracing mechanism coupled with runtime data collection to generate multidimensional performance metrics. These metrics are then presented through synchronized multi-view visualizations that capture comprehensive vGPU behavioral characteristics. By enabling detailed insight into vGPU execution dynamics, this approach significantly enhances observability and debugging efficiency in GPU-virtualized environments, effectively filling a longstanding void in vGPU performance analysis tooling.

Abstract

The graphics processing unit (GPU) plays a crucial role in boosting application performance and enhancing computational tasks. Thanks to its parallel architecture and energy efficiency, the GPU has become essential in many computing scenarios. On the other hand, the advent of GPU virtualization has been a significant breakthrough, as it provides scalable and adaptable GPU resources for virtual machines. However, this technology faces challenges in debugging and analyzing the performance of GPU-accelerated applications. Most current performance tools do not support virtual GPUs (vGPUs), highlighting the need for more advanced tools. Thus, this article introduces a novel performance analysis tool that is designed for systems using vGPUs. Our tool is compatible with the Intel GVT-g virtualization solution, although its underlying principles can apply to many vGPU-based systems. Our tool uses software tracing techniques to gather detailed runtime data and generate relevant performance metrics. It also offers many synchronized graphical views, which gives practitioners deep insights into GVT-g operations and helps them identify potential performance bottlenecks in vGPU-enabled virtual machines.

Problem

• GPU virtualization
• performance analysis
• virtual GPU
• debugging
• vGPU

Innovation

• GPU virtualization
• performance analysis
• software tracing
• vGPU
• GVT-g