Migrating to GPU Trace: A Guide to Better Graphics Performance
Summary
NVIDIA Nsight Graphics offers a powerful toolset for debugging and profiling graphics applications. One of its key features is GPU Trace, a frame-level profiler that provides detailed insights into GPU performance. This article guides developers through the process of migrating from Range Profiler to GPU Trace, highlighting the benefits and improvements of the latter.
Introduction
Graphics development is becoming increasingly complex, with the need for high-performance rendering and detailed profiling. NVIDIA Nsight Graphics addresses this need with GPU Trace, a comprehensive profiling tool that helps developers identify and optimize performance bottlenecks. In this article, we’ll explore the transition from Range Profiler to GPU Trace and how it can enhance your graphics development workflow.
Understanding GPU Trace
GPU Trace is a frame-level profiler that captures detailed performance data on a single pass. It leverages the performance monitor components (PMS) on NVIDIA GPUs to collect data with minimal intervention in application execution. This makes it a low-overhead, non-intrusive profiler that provides accurate insights into GPU performance.
Key Features of GPU Trace
- Comprehensive API Event Tracking: GPU Trace includes an Events List that provides detailed API event tracking along with marker correlation and timing information. Selecting events creates selection regions in the timeline, allowing for visual inspection of API state.
- Automated Performance Analysis: Trace Analysis automatically identifies performance blockers, highlighting the top three performance issues per marker for quick identification of optimization opportunities.
- CUDA Events Support: GPU Trace supports CUDA Events, including NGX shaders attribution, providing insights into CUDA operations directly on the GPU Trace timeline.
- Real-Time HUD: The Real-Time HUD offers real-time performance monitoring, helping developers uncover frame spikes and bottlenecks.
Migrating from Range Profiler to GPU Trace
Migrating to GPU Trace from Range Profiler involves understanding the new features and capabilities of GPU Trace. Here are some key steps to consider:
- Familiarize Yourself with GPU Trace: Start by exploring the GPU Trace interface and its various components, including the Events List, Trace Analysis, and CUDA Events support.
- Understand the Benefits: Recognize the improvements GPU Trace offers over Range Profiler, such as comprehensive API event tracking and automated performance analysis.
- Integrate GPU Trace into Your Workflow: Incorporate GPU Trace into your development process to leverage its detailed profiling capabilities.
Practical Tips for Using GPU Trace
- Use Event Markers: Insert event markers into your code to trace exceptions in DirectX 12 or Vulkan applications, providing more context in case of a GPU exception.
- Analyze Performance Metrics: Collect performance metrics for RT Cores to uncover frame spikes and bottlenecks in ray tracing applications.
- Visualize Memory Migrations: Use GPU Resource Migration Trace to eliminate memory overcommitment stutters and resource priority issues.
Additional Resources
For more information on NVIDIA Nsight Graphics and GPU Trace, visit the NVIDIA Developer website.
Table: Key Features Comparison
| Feature | Range Profiler | GPU Trace |
|---|---|---|
| API Event Tracking | Limited | Comprehensive with Events List |
| Performance Analysis | Manual | Automated with Trace Analysis |
| CUDA Events Support | No | Yes, with NGX shaders attribution |
| Real-Time Monitoring | No | Yes, with Real-Time HUD |
Table: Practical Tips for GPU Trace
| Tip | Description |
|---|---|
| Use Event Markers | Insert markers for exception tracing in DirectX 12 or Vulkan apps. |
| Analyze Performance Metrics | Collect metrics for RT Cores to identify frame spikes and bottlenecks. |
| Visualize Memory Migrations | Use GPU Resource Migration Trace to eliminate memory overcommitment stutters. |
Conclusion
Migrating to GPU Trace from Range Profiler is a straightforward process that offers significant benefits for graphics developers. With its comprehensive API event tracking, automated performance analysis, and CUDA Events support, GPU Trace provides a powerful toolset for optimizing graphics performance. By integrating GPU Trace into your workflow, you can identify and address performance bottlenecks more effectively, leading to better overall graphics quality.