We can see on which GPU each kernel ran, as well as the grid dimensions used for each launch. In addition to summary mode, nvprof supports GPU-Trace and API-Trace modes that let you see a complete list of all kernel launches and memory copies, and in the case of API-Trace mode, all CUDA API calls.įollowing is an example of profiling the nbody sample application running on two GPUs on my PC, using nvprof -print-gpu-trace.
The summary groups all calls to the same kernel together, presenting the total time and percentage of the total application time for each kernel. In its default summary mode, nvprof presents an overview of the GPU kernels and memory copies in your application. myApp, I can quickly see a summary of all the kernels and memory copies that it used, as shown in the following sample output. Sometimes this is just a sanity check: is the app running kernels on the GPU at all? Is it performing excessive memory copies? By running my application with nvprof. I often find myself wondering if my CUDA application is running as I expect it to. But nvprof is much more than that to me, nvprof is the light-weight profiler that reaches where other tools can’t. At first glance, nvprof seems to be just a GUI-less version of the graphical profiling features available in the NVIDIA Visual Profiler and NSight Eclipse edition.
nvprof is a command-line profiler available for Linux, Windows, and OS X. CUDA 5 added a powerful new tool to the CUDA Toolkit: nvprof.
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