![]() If you have any questions about the commenting policy, please let us know through the Contact Page.VideoCardz Moderating Team reserves the right to edit or delete any comments submitted to the site without notice.Please also note that comments that attack or harass an individual directly will result in a ban without warning. A failure to comply with these rules will result in a warning and, in extreme cases, a ban.Comments complaining about the article subject or its source will be removed.If you are concerned about performance, look into using editing codecs or a proxy workflow if you are having performance issues. 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. The data on this chart is calculated from Geekbench 5 results users have uploaded to the Geekbench Browser. CUDA is also only available on Nvidia GPUs. Welcome to the Geekbench CUDA Benchmark Chart. They showed that there are minimal modications involved when converting a CUDA kernel to an OpenCL kernel. Note this may include abusive, threatening, pornographic, offensive, misleading, or libelous language. CUDA on and Nvidia GPU works better then OpenCL on an Nvidia GPU. 18 compared the performance of CUDA and OpenCL using complex, near-identical kernels. Comments and usernames containing language or concepts that could be deemed offensive will be deleted. ![]() Discussions about politics are not allowed on this website. Including a link to relevant content is permitted, but comments should be relevant to the post topic. While the OpenCL which is supported in more applications does not give the same performance boosts where supported as CUDA does. Comments deemed to be spam or solely promotional in nature will be deleted. CUDA being a proprietary NVIDIA framework is not supported in as many applications as OpenCL, but where it is supported, the support makes for unparalleled performance.I found reviews which showed a 980 Ti running very well in OpenCL tests, far better than benchmarks from a year earlier. OpenCL performance on its products in recent months, ie. This is a link on previous sorting algorithms test. If the applications you use split their support between CUDA and OpenCL we recommend using a recent Nvidia card. If you will stick to NVIDIA GPUs anyways you will likely get better performance using directly CUDA. CUDA vs OpenCL vs SPU Part IV Finally Ive got radix sort implementation which is working on AMD OpenCL. We make an extensive analysis of the performance gaps taking into account programming models, ptimization. If platform independence is important OpenCL might be interesting for you since you then can take the code and run it on a complete other hardware for comparison. We have selected 16 benchmarks ranging from synthetic applications to real-world ones. If you do not use any "classic" rendering, which you wanna combine your path traced results, I see not why you should use compute shaders for a pure path tracer. But when they for example used image processing algorithms that can be parallized very efficiently from the GPU then the comparison is nearly worthless compared what you might do in your path tracing code. In their comparisons did CUDA often show good results. Here is a quick comparison of a GPU versus CPU sample project in NeuroSolutions using one AMD Radeon (OpenCL) and three various NVIDIA (CUDA) graphics cards. Some people did already the effort to measure differences. It depends, as always on your setup and use case. NVIDIA develops their own ray tracer based on CUDA and GPU computing. OpenCL is not only built for GPUs and can also be used to write codeįor example to run massive parallel computations on supercomputers. OpenCL is a parallel programming compute API and is vendor independentĬompute Shaders are a way to perform general purpose computations withinĪ rendering system but without being part of a fixed rendering architecture.īut also for other graphics APIs like DirectX (DirectCompute) etc. OpenGL is a graphics specific API and is vendor independent C++ OCL cpu OpenCL is a framework for writing programs that execute across heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors or hardware accelerators. OpenCL, but has be less succesfull in crippling OpenCL in favor of Metal. Apple is trying to do the same thing with Metal vs. GPGPU stands for General Purpose computing on GPUsĬUDA is the specific NVIDIA API to perform GPGPU only on their hardware For Davinci Resolve it doesnt matter if its OpenCL, Cuda or Metal and leaves it up to the manufacturer (in this case NVidia) to cripple OpenCL in favor of CUDA. To generate the CPU results I simply ran the CUDA performance tests with CUDA disabled, so that the fall back CPU functions were called, by changing the following. The FFT single-precision test was also noticeably much faster with CUDA. To first clear your confusion around the terms: Software: OpenCV 3.4 compiled on Visual Studio 2017 with CUDA 9.1, Intel MKL with TBB, and TBB.
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