Measuring GPU Occupancy in Multi-Stream Workloads: A Deep Dive
Summary: Understanding GPU occupancy is crucial for optimizing the performance of multi-stream workloads. This article delves into the challenges of measuring GPU occupancy and introduces a method using NVIDIA Nsight Systems to analyze and improve GPU utilization. We explore the importance of GPU metrics, how to interpret them, and provide practical examples to help developers optimize their workloads.
The Challenge of GPU Occupancy
NVIDIA GPUs have become increasingly powerful with each new generation, offering more streaming multi-processors (SMs) and faster memory systems. However, this growing concurrency creates a challenge: workloads must expose a corresponding level of concurrency to saturate GPU resources. A common approach to achieve this is by sending independent tasks to the GPU using multiple streams or the CUDA Multi-Process Service.
GPU Metrics to the Rescue
NVIDIA Nsight Systems is a performance analysis tool that helps determine whether chunks of work, called kernels, are executing on the GPU. By enabling the GPU Metrics feature, Nsight Systems samples counters on each GPU during profiling to identify limiters and gather statistical information on the behavior of the application.
Understanding GPU Metrics
GPU metrics provide valuable insights into how well the GPU is utilized. Key metrics include:
- SM Active: The percentage of all SMs in use during each sample period.
- Gross GPU Utilization: The average percentage of all SMs in use during the entire workload.
- Net GPU Utilization: The average percentage of SMs in use during measurable kernel executions.
- Effective GPU Utilization Time: The effective amount of time the GPU would have been fully in use had all samples been condensed.
Interpreting GPU Metrics
To interpret these metrics, consider the following:
- Gross GPU Utilization gives an overall idea of how often the GPU is in use during the execution of the workload.
- Net GPU Utilization provides a more accurate picture by excluding sections where no kernels are being launched.
- Effective GPU Utilization Time helps compare the actual utilization time to the duration of the Nsight Systems timeline.
Practical Example
Consider a workload with multiple streams. Using Nsight Systems, we can analyze the GPU metrics to understand how well the GPU is utilized.
| Metric | Value |
|---|---|
| Gross GPU Utilization | 70 |
| Net GPU Utilization | 77 |
| Effective GPU Utilization Time | 2 seconds |
In this example, the gross GPU utilization is 70%, indicating that the GPU is in use 70% of the time during the entire workload. The net GPU utilization is 77%, showing that during measurable kernel executions, the GPU is utilized more efficiently. The effective GPU utilization time is 2 seconds, which can be compared to the duration of the Nsight Systems timeline to assess how well the SMs are used.
The Future of GPU Analysis
New features are continually added to NVIDIA analysis tools. Recent developments include recipes for convenient analysis of cluster-level applications and performance regression studies. Additionally, NVIDIA is focusing on providing access to Nsight System’s GPU performance details through Python scripting, making it easier for developers to analyze and optimize their workloads.
Conclusion
Measuring GPU occupancy in multi-stream workloads is crucial for optimizing performance. By using NVIDIA Nsight Systems and understanding GPU metrics, developers can gain valuable insights into how well their workloads utilize the GPU. This article has provided a practical guide to analyzing GPU occupancy, helping developers to optimize their workloads and achieve better performance.