![]() In Nsight Systems, CPU sampling, OS Runtime, API tracing, or adding NVTX instrumentation and tracing NVTX can help you figure out what the CPU is doing between CUDA API calls. nvprof -version nvprof: NVIDIA (R) Cuda command line profiler Copyright (c) 2012 - 2019. Where myKernel is the name of my CUDA kernel which has some input arguments given by args. Just above the CUDA API timeline, the thread’s state indicates that it is busy, so the CPU is executing some other operations. 3382747 cudaFree 591094 cudaMalloc 34042 cudaLaunch. I'm using the following command line: nvprof -metrics flops_sp -metrics flops_sp_add -metrics flops_sp_mul -metrics flops_sp_fma -metrics flops_sp_special myKernel args However when I run, the result of flop_count_sp (which is supposed to be flop_count_sp_add + flop_count_sp_mul + flop_count_sp_special + 2 * flop_count_sp_fma) I find that it does not include in the summation the value of flop_count_sp_special.Ĭould you suggest me what I am supposed to use? Should I add this value to the sum of flop_count_sp or I should consider the formula does not include the value of flop_count_sp_special?Īlso could you please tell me what are these special operations? Each multiply-accumulate operation contributes 2 to the count." it says flop_count_sp is "Number of single-precision floating-point operations executed by non-predicated threads (add, multiply, multiply-accumulate and special). at which point nvprof will show us the profiling results for our function. Also when I browse through the documentation (here. We strongly recommend switching to hardware-accelerated GPU scheduling mode when running WSL2. Since my 38GB cloud disk only has 6 GB available, nvprof crashes. Here, the benefits of hardware-accelerated GPU scheduling can offset the latency-induced performance loss, as CUDA adopts the same submission strategy followed on native Linux for both WSL2 and native Windows. I am trying to profile a plugin for Clang-7 that performs instruction scheduling by launching a kernel to perform ACO scheduling. I recently updated to an RTX 3080 in my environment and can no longer use nvprof as I had before. ![]() Returns: cudaSuccess, cudaErrorInvalidDeviceFunction, cudaErrorInvalidConfiguration, cudaErrorLaunchFailure, cudaErrorLaunchTimeout, cudaErrorLaunchOutOfResources, cudaErrorSharedObjectInitFailed Note: Note that this function may also return error codes from previous, asynchronous launches.I see that nvprof can profile the number of flop in the kernel (using the parameters as below). The second nvprof needs to write a 12GB temporary file to /tmp before it can proceed. Hello, I am having a hard time profiling my instruction scheduling kernel using Nvidia Nsight Compute. Device char string naming device function to execute If you started Legacy CUDA debugging: Youll notice that on the host machine, a pop-up message indicates that a connection has been made. As you have pointed out, you can use CUDA profilers to profile python codes simply by having the profiler run the python interpreter, running your script: nvprof python. Show/hide this icon group by right-clicking on the Visual Studio toolbar and toggling Nsight Connections. cudaLaunch() must be preceded by a call to cudaConfigureCall() since it pops the data that was pushed by cudaConfigureCall() from the execution stack. Click on the Start CUDA Debugging (Legacy)/ (Next-Gen) toolbar menu item. That means that the CPU thread launches the kernel but does not wait for the kernel to complete. The NVIDIA Visual Profiler is available as part of the CUDA Toolkit. Note that you mention NVPROF but the pictures you are showing are from nvvp - the visual profiler. First introduced in 2008, Visual Profiler supports all 350 million+ CUDA capable NVIDIA GPUs shipped since 2006 on Linux, Mac OS X, and Windows. The parameter specified by entry must be declared as a _global_ function. The NVIDIA Visual Profiler is a cross-platform performance profiling tool that delivers developers vital feedback for optimizing CUDA C/C++ applications. ![]() After that you have just to enter the file you want to profile (test.x) and the software runs and profiles your program. You have just to run nvvp in the terminal and it opens NVIDIA Visual Profiler. Development and compiling (nvcc compiler) are used on Go. There is an executable file, nvvp in usr/local/cuda/bin. The parameter entry must be a character string naming a function that executes on the device. Demo on howto use nvprof, NVIDIA Nsight Systems and Nsight Compute to profile and analyse CUDA code. /rebates/&252fcudalaunch-nvprof. Launches the function entry on the device. ![]()
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