虚拟化环境中的GPU性能分析：一项案例研究
Analyzing GPU Performance in Virtualized Environments: A~Case Study
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中文总结 AI 辅助
本文针对当前性能工具不支持虚拟GPU（vGPU）的问题，提出一款兼容Intel GVT-g的新型性能分析工具，通过软件追踪技术收集数据并生成指标，助力识别vGPU虚拟机的性能瓶颈。
中文摘要 AI 辅助
图形处理单元（GPU）在提升应用性能和增强计算任务方面发挥着关键作用。得益于其并行架构和能效，GPU已成为许多计算场景的核心组件。另一方面，GPU虚拟化的出现是一项重大突破，它为虚拟机提供了可扩展且适应性强的GPU资源。然而，这项技术在调试和分析GPU加速应用的性能方面面临挑战。当前大多数性能工具不支持虚拟GPU（vGPU），凸显了对更高级工具的需求。因此，本文介绍了一款专为使用vGPU的系统设计的新型性能分析工具。该工具与Intel GVT-g虚拟化解决方案兼容，尽管其底层原理可应用于许多基于vGPU的系统。该工具使用软件追踪技术收集详细的运行时数据并生成相关性能指标，还提供多个同步图形视图，使从业者能深入了解GVT-g的运行情况，帮助他们识别启用vGPU的虚拟机中潜在的性能瓶颈。
英文摘要
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.
