The issue is that it has validation data only for GPU, see sections …. This will provides a guide to the expected performance of a function irrespective of the specific configuration. Your email address will not be published. Why is egrep ignoring the negative whitespace? But just as OpenGL and DirectX, one is not under the other or viceversa. For example the performance test for the cv::cuda::pow() can be found here. CUDA vs OpenCL performance comparison. This combined with a higher average performance for all GPU’s tested, implies that you should nearly always see an improvement when moving to the GPU, if you have several OpenCV functions in your pipeline (as long as you don’t keep moving your data to and from the GPU), even if you are using a low end two generation old laptop GPU (730m). Can I target the CPU via OpenCL while developing with CUDA toolkit OpenCL sdk? If you had to build the ultimate OpenCV/Cuda rig, would you go with an i9-7980XE and Titan Vs or would you go with dual Xeons and Tesla V100s? Thank you for your comment, I had considered including all the code for generating the results, however the performance tests require python, and the per-compiled OpenCV binaries rely on CUDA 9.1 being installed or re-distribution of some of its dll’s, which I am not sure if I can host. Why it's news that SOFIA found water when it's already been found? In any case, thank you very much for your quick help. The full specifications are shown below, where I have also included the maximum theoretical speedup, if the OpenCV function were bandwidth or compute limited. Software: OpenCV 3.4 compiled on Visual Studio 2017 with CUDA 9.1, Intel MKL with TBB, and TBB. the tests for the cudaarithm python modules are in test_cudaarithm. Are there any valid continuous Sudoku grids? They only read and write global memory, each thread different location. That said even the slowest configurations on the slowest GPU’s are in the same ball park, performance wise, as the fastest configurations on the most powerful CPU’s in the test. don’t have any impact on performance. The timings are not so good compared to GPU (as expected). To conclude I would just reiterate that, the benefit you will get from moving your processing to the GPU with OpenCV will depend on the function you call and configuration that you use, in addition to your processing pipeline. Hi, unfortunately I have not run a comparison of the OpenCV routines which utilize MKL + TBB vs their CUDA counterparts. I can do the tests on other cards. The data on this chart is calculated from Geekbench 5 results users have uploaded to the Geekbench Browser. The differences you observed are likely due to subtle differences in the memory access patterns between the two kernels that result from different optimizations made by the OpenCL vs CUDA toolchain. I am trying to reproduce the results. Active 5 years, 3 months ago. Finally lets examine which OpenCV functions took the longest. Main points to state this is that the libraries are different, the compilers are different, and the execution model is different as well. If you want to verify this for a particular function you are interested in then you can check the source code. They both use the same HW in the end. On the guitar, why the treble strings should be wound clockwise while the bass strings should be wound counterclockwise? If you are on a 64-bit platform, my first guess would be that the OpenCL kernel is benefiting from the lower register pressure since it can be 32-bit. Although OpenCL promises a portable language for GPU programming, its generality may entail a performance penalty. All the results are generated using the inbuilt OpenCV performance tests. The launch configuration for CUDA is 200 blocks of 250 threads (1D), which corresponds directly to the configuration for OpenCL - 50,000 global work size and 250 local work size. That said it is simple enough to do it yourself. @”The OPENCV_TEST_DATA_PATH points to the correct test data on my computer.” I then just copied the folder and regenerated cudaXXX.xml files for the CPU run from the CPU compiled cuda perf tests opencv_perf_cudaXXX.exe and everything worked, as long as I remembered to switch the environmental variable from to point to the correct test results. Why is "iron" pronounced "EYE-URN" but not "EYE-RUN"? To make sure the results accurately reflect the average performance of each GPU, the chart only includes GPUs with at least five unique results in the Geekbench Browser. That said the above results also show, that some of these slower functions, do benefit from the parallelism of the GPU, but a more powerful GPU is required to leverage this. OpenCL NDRange dimensions order bug on nVidia? Why did the Old English word "līċ" get displaced by "corpse"? Is this possible or am I timing it wrong? OpenCL FFT on both Nvidia and AMD hardware? I am using CUDA 6.0 and the OpenCL implementation that comes bundled with the CUDA SDK. Higher scores are better, with double the score indicating double the performance. Welcome to the Geekbench CUDA Benchmark Chart. 1867 Yonge St. Suite 902Toronto, Ontario, CanadaM4S 1Y5. In this post I am going to use the OpenCV’s performance tests to compare the CUDA and CPU implementations. Viewed 21k times 18. You mentioned that the speedup time is OpenCV function execution time. The OpenCL code runs faster. Yes, it is there. If you are not familiar with this concept then I would recommend watching Memory Bandwidth Bootcamp: Best Practices, Memory Bandwidth Bootcamp: Beyond Best Practices and Memory Bandwidth Bootcamp: Collaborative Access Patterns by Tony Scudiero for a good overview. As you can see there it is very important to build with MKL + TBB if you are using BLAS routines. Hi , I need to accelarate my opencv python application using CUDA. It is basically what AMD uses in their GPUs for GPU acceleration (CUDA is a proprietary technology from Nvidia! I have two identical kernels for each platform (they differ in the platform specific keywords). Can containers that held spoiled food be cleaned and be safe again? This means that the performance of a given kernel usually depends largely on the memory access patterns exhibited by the given algorithm.