Cuda vs opencl benchmark driver#
Cuda vs opencl benchmark pro#
Cuda vs opencl benchmark code#

Focus 69, Intensity 35 in Spotlight light mode. Border Thickness: 3 and Light Intensity: 4 Light Direction, top right with invert texture applied.ДЄ very intensive filter which seen recent optimisations in CS2/3. Texture: 1ДЄ canvas filter follow up to the first with a variation of settings. Radius is set to 3 and Threshold is set to 36.ДЄn artistic filter to create a realistic watercolor image.Д«rush detail: 11. Light Intensity: 11.ДЄ filter which is used quite widely by professionals scanning less than perfect original flat copy. Important test for web designers as this colour mode is used for webpages and will be the mode of choice for digital images raw from the camera.ДЄ relatively demanding test which applies a brush stroke filter to the image. Digital photographs will be converted to this mode for final output. Important for all the professionals as this 4 plate (cyan, magenta, yellow and black (k-key colour)) colour mode is used in graphics bureaus and printing presses for colour reproduction to newsprint or magazine. Light direction, bottom without invert texture. The V3 suite comprises the following tests:ДЄ canvas filter with scaling set to 112 and relief 10.

Cuda vs opencl benchmark pro#

Notice a trend regarding GPU Max Power Consumption in this comparison? Notice that the Premiere Pro Next Performance differences are relatively small, all things considered? Perhaps the biggest flaw in this comparison (other than the skewed max power consumption) is that the Premiere Pro Next Performance barely changes when moving from one GPU to another, even when comparing different GPU's from the same manufacturer. > 88.9% higher Street Price for FirePro model > 16% higher Premiere Pro Next Performance for FirePro model > 124.6% higher Max Power Consumption for FirePro model

> 19.5% lower Street Price for FirePro modelДЇirePro W9000 (6GB GDDR5 RAM, $3399 USD): 274w > 8% higher Premiere Pro Next Performance for FirePro model > 84.4% higher Max Power Consumption for FirePro model > 4.9% lower Street Price for FirePro model > 12% higher Premiere Pro Next Performance for FirePro model > 87.5% higher Max Power Consumption for FirePro model > 4.7% higher Street Price for FirePro model > 27% higher Premiere Pro Next Performance for FirePro model > 47.1% higher Max Power Consumption for FirePro model

Cuda vs opencl benchmark driver#

The point of this comparison, however, is that managed memory performs very well using the standard cuBlas function.Click to expand.The specified Max Power Consumption, Adobe Premiere Pro Next Performance (on pre-release software which may or may not be performance optimized in the GPU driver software), and Street Price for each Quadro card and each FirePro card used in the comparison is as follows: Also note that cuBlasXt has more functionality (multiple cards), so the slightly worse performance is not surprising. Note that cublasXtSgemm() is designed to run on host allocated memory and handles optimized tiled memory transfers. the cublasXtSgemm() function running on host allocated memory. This benchmark compares the cublasSgemm() function running on managed memory vs. Host: access all arrays a second time: 0.000017Д¬uBlasXt handles out-of-core computations for memory that is allocated with cudaMallocHost(). Host: access all arrays a second time: 0.000045 With N=200000000, explicit memory performs slightly better, but DMA is faster than both:

Most of the time is spent in copying, the kernel runtime is negligible.

This benchmark tests initializing three arrays in host memory, running a kernel and accessing the result. The examples were run on Linux with Cuda release 9.2, V9.2.148, using 16GB of host memory and a GeForce 1060 with 6GB of GPU memory. Benchmarks that show the superiority of explicit memory management are welcome. It is incomplete and does not yet address scenarios like IPC or multiple GPUs.

Cuda vs opencl benchmark code#

This benchmark suite attempts to provide actual code so that people can check for themselves. Recent reports appear to address only specialty scenarios like 8 NVlink GPUs. The reports usually do not come with links to actual code. There have been several reports, many of them from around 2014, that unified memory has a lower performance in many scenarios. Game consoles can have tighter hardware control, but Nvidia also has the Jetson product line with physically unified memory that has been reported to have better performance than explicit memory management. Starting with the Pascal architecture, Nvidia also offers advanced unified memory support. It simplifies game development because it frees the programmer from having to track whether a memory block is on CPU or GPU memory. Unified memory has been a feature of game consoles for many years.