Understanding Overhead and Latency in NVIDIA Nsight Systems
Summary: NVIDIA Nsight Systems is a powerful tool for analyzing and optimizing the performance of applications running on NVIDIA GPUs. This article delves into the visualization of overhead and latency in Nsight Systems, providing insights into how developers can use this tool to identify and address performance bottlenecks in their applications.
What is NVIDIA Nsight Systems?
NVIDIA Nsight Systems is a system-wide performance analysis tool designed to visualize an application’s algorithms, identify optimization opportunities, and tune performance to scale efficiently across CPUs and GPUs. It provides a unified timeline view of system workload metrics, allowing developers to investigate correlations, dependencies, activity, bottlenecks, and resource allocation.
Understanding Overhead and Latency
Overhead and latency are critical concepts in performance analysis. Overhead refers to the time it takes to perform operations that ideally should take zero time, limiting the rate at which these operations can be performed. Latency, on the other hand, is the time between requesting an asynchronous task and beginning to execute it.
CPU Overhead
CPU overhead includes the time it takes for the CPU to launch a CUDA kernel, including any mutex-lock contention that occurs in the driver if doing multi-threaded launching. This can be visualized in Nsight Systems as the full duration of the kernel launch API call.
GPU Launch Overhead
GPU launch overhead is the time it takes for the GPU to retrieve the command and begin executing it. This can include context switches, where the GPU may have a different context active and require a switch to get back to working on the application.
Host to Device Memory Overhead
Host to device memory overhead occurs when data is copied from the host CPU to the GPU. This can be visualized in Nsight Systems as the time range from the API call to enqueue the commands to copy the input data to the GPU’s memory until the copy is finished.
Nsight Systems Overhead
Nsight Systems itself adds a bit of overhead to capture trace data. Events may appear longer in the timeline than they would take when the app runs without the tool. This overhead is typically less than a microsecond.
Visualizing Overhead and Latency in Nsight Systems
Nsight Systems provides a detailed timeline view of system activity, allowing developers to visualize overhead and latency. The timeline includes CPU and GPU activity, events, annotations, throughput, and performance metrics.
CPU Timeline
The CPU timeline shows the activity of the CPU, including the time spent launching CUDA kernels. Gaps in the CPU timeline can indicate that the CPU is executing other operations, which can be investigated using CPU sampling, OS Runtime API tracing, or adding NVTX instrumentation and tracing NVTX.
GPU Timeline
The GPU timeline shows the activity of the GPU, including the time spent executing CUDA kernels. Gaps in the GPU timeline can indicate that the GPU is executing another context or is idle while waiting for more work to be scheduled.
Case Study: Understanding Overhead and Latency in a CUDA Application
Consider a CUDA application that performs a series of kernel launches. Using Nsight Systems, we can visualize the overhead and latency associated with these launches.
| Event | Duration |
|---|---|
| Kernel Launch API Call | 10 µs |
| GPU Execution | 100 µs |
| Memory Copy | 20 µs |
In this example, the kernel launch API call takes 10 µs, which is the CPU overhead. The GPU execution takes 100 µs, which is the latency. The memory copy takes 20 µs, which is the host to device memory overhead.
Conclusion
Understanding overhead and latency is critical for optimizing the performance of applications running on NVIDIA GPUs. Nsight Systems provides a powerful tool for visualizing and analyzing these concepts, allowing developers to identify and address performance bottlenecks in their applications. By using Nsight Systems to visualize overhead and latency, developers can optimize their applications to achieve better performance and scalability.