If length=33,the kernel time is: kernel time: 0. If length=32,the kernel time is: kernel time: 37.341919 (ms) Later, I will attach the timing code.What causes such a result? dim3 dimBlock(length, length) I first suspect that my timing code is wrong, but after checking, I think there is no mistake. CudaLaunch looks and feels the same on every platform and provides fast, Java-independent access. When length> 32, the time spent by the kernel suddenly decreases 100-1000 times. Release notes for version 2.6.1: Support for new functionality introduced in the 8.0. CudaLaunch is an application for Windows, macOS, iOS, and Android devices that provides mobile workers secure remote access through a Barracuda CloudGen Firewall to their organization’s private cloud applications and other sensitive information. There is more, please see the features below for the details. When length >Ĭhanges according to the regularity of the size of length. Xdebug profile files (cachegrind format) can be opened, viewed, and inspected.The extension also highlights hot paths in your code, according to the profiling results. In addition, extra information from the profile is added for use by CUDA professionals, such as CUDA launch parameters. The program will calculate an length * length matrix A, the calculation of each element of A is done by one thread, ngridDim is set to (1,1). Why the wrong program can get the correct result? 1 310.04us 310.04us 310.04us cuModuleLoadData 0.03 234.87us 24 9.7860us 5. I was trying to profile my code using nvidia visual profiler but every time I ran it showed me the. First introduced in 2008, Visual Profiler supports all 350 million+ CUDA capable NVIDIA GPUs shipped since 2006 on Linux, Mac OS X, and Windows. If I run the program without debug, the program runs smoothly and the result of the calculation is the same as using the CPU (without parallelism), indicating that my calculations are correct. Non-Visual Profiler nvprof python trainmnist.py I prefer to use -print-gpu-trace. The NVIDIA Visual Profiler is a cross-platform performance profiling tool that delivers developers vital feedback for optimizing CUDA C/C++ applications. (my program is using openmp multithreads) I’m seeing that cudaLaunch takes 280 ns (min) and 58ms (max). When I debugged with cuda-gdb and Nsight, there was an expected "cudaLaunch returned (0x9)" error. And I need some help I’m profiling my CUDA program, using nvprof. When I call the kernel function, I know that block_len must be 1024. I wrote a CUDA program, I have two questions about this program.
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