Templating in openCL you have to create new kernels for every data-type. Hardware texture interpolation OpenCL has to fall back to a larger kernel or OpenGL. Atomic operations which make double-write threads easier to implement.
#CUDA VS OPENCL BENCHMARK CODE#
For the programmer, it’s a little bit harder to code for. It’s a minor philosophical distinction, but there’s a quantifiable difference in the end. And now we have new more interesting results :) Time of GPU sorting includes time of data downloading from video memory. Whereas CUDA uses the graphics card for a co-processor, OpenCL will pass off the information entirely, using the graphics card more as a separate general purpose peer processor. This is a link on previous sorting algorithms test. If you have any questions about the commenting policy, please let us know through the Contact Page.Finally I've got radix sort implementation which is working on AMD OpenCL.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.Note this may include abusive, threatening, pornographic, offensive, misleading, or libelous language. 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. Comments deemed to be spam or solely promotional in nature will be deleted.
#CUDA VS OPENCL BENCHMARK SERIES#
NVIDIA GeForce RTX 30 Series Specifications Reviewers are expected to post their benchmarks of the Founders Edition on September 14th, with custom models reviews 3 days later. OpenCL is an open standard that can be used to program CPUs, GPUs, and other devices from different vendors, while CUDA is specific. The graphics card will officially go on sale on September 17th. CUDA and OpenCL are two different frameworks for GPU programming. The graphics card is paired with 10GB of next-generation GDDR6X memory across 320-bit memory bus. NVIDIA GeForce RTX 3080 features 8nm GA102-200 GPU with 8704 CUDA cores.
NVIDIA GeForce RTX 3080 OpenCL Performance The RTX 3080 has on average 168% of RTX 2080 SUPER performance and 138-141% performance of the RTX 2080 Ti. The average for both tests, however, is more or less the same. We gathered the data for both CUDA and OpenCL tests. NVIDIA GeForce RTX 3080 on CompubenchĬU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH Especially when we take all the new hardware to accelerate technologies (such as RTX or DLSS) into account. These types of tests are compute oriented and do not illustrate gaming performance at all. The CUDA info page confirms its a 320-bit memory configuration with 19 Gbps modules. This is either the Founders Edition model tested by one of the reviewers or a custom variant featuring the same clock speed. The OpenCL info page reveals its a 10GB variant with 68 Compute Units (Streaming Multiprocessors) and 1710 MHz boost frequency. Hence, it is recommended to use CUDA-based. The GeForce RTX 3080 graphics card has been tested using 456.16 drivers. Note that due to limitations of the NVIDIA OpenCL compiler CUDA is still superior in performance on NVIDIA GPUs. The GeForce RTX 3080 has been put through a number of CUDA and OpenCL tests with the Compubench benchmark suite. The FFT single-precision test was also noticeably much faster with CUDA. NVIDIA GeForce RTX 3080: 168% of RTX 2080 performance
The first post-announcement benchmark of the GeForce RTX 3080 graphics cards.
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