Am Mon, 16 Dec 2019 13:01:32 +0100 schrieb Holger Wünsche <[email protected]>:
> Hi Jochen, > > > just looking at the numbers on Wikipedia [1] the RTX2060 Super has > approx 60% more computation power (GFLOPS) and double the memory > bandwidth. However I don't know if you would see speed improvements > using another GPU, because the bottleneck might be something else. If > you want you can send me (not per email ;) ) one of your images (and > xmp-file) and I export it on my computer (i7 6700k, RTX2060 (non > Super)) and measure the time it takes to export. > > Others might know how to find the bottleneck and tell you what limits > your export times Hi, after searching a lot in the web I decided to buy a NVIDIA Corporation TU116 [GeForce GTX 1660]. Note: the more expensive TI is not remarkable faster, nearly the same speed, depends on the photo. I think this card has the best price-value. If you are not adventurous with Linux, use Nvidia and / or search for AMD and troubles. It doesn't help if you read it will get better and what could be maybe. Note the differences if you compare. At the end your system must work now and not maybe some day, IMHO the cpu is not so important, if you compare the GPU. Would be interested how long the RTX2060 takes with the bench-file. Try to install the phoronix test suite to get compareable test-files and do something like below: You can download the bench-file here, but the phoronix suite contains more test files to compare: https://math.dartmouth.edu/~sarunas/bench_raw/ $ darktable-cli bench.srw bench.srw.xmp bench.jpg --core -d perf -d opencl ... 0.147530 [opencl_init] device 0 `GeForce GTX 1660' has sm_20 support. 0.147660 [opencl_init] device 0 `GeForce GTX 1660' supports image sizes of 32768 x 32768 0.147663 [opencl_init] device 0 `GeForce GTX 1660' allows GPU memory allocations of up to 1485MB [opencl_init] device 0: GeForce GTX 1660 GLOBAL_MEM_SIZE: 5942MB MAX_WORK_GROUP_SIZE: 1024 MAX_WORK_ITEM_DIMENSIONS: 3 MAX_WORK_ITEM_SIZES: [ 1024 1024 64 ] DRIVER_VERSION: 435.21 DEVICE_VERSION: OpenCL 1.2 CUDA ... 5,678742 [opencl_profiling] spent 2,4782 seconds totally in command queue (with 0 events missing) 5,678749 [dev_process_export] pixel pipeline processing took 3,343 secs (8,930 CPU) [export_job] exported to `bench_02.jpg' 6,009675 [opencl_summary_statistics] device 'GeForce GTX 1660' (0): 551 out of 551 events were successful and 0 events lost Here is the result of the cpu only, I use a Ryzen 3700X: 18,169530 [dev_process_export] pixel pipeline processing took 16,369 secs (230,259 CPU) So using the GPU is about 5 times faster for me. Read for more tests: https://math.dartmouth.edu/~sarunas/darktable_bench.html What I found out, that it is getting a lot more expensive, if you want significant more speed, but I cannot give you an advice what do use, if you want to spend less money. Ask always for people who use this card really and don't talk about theory. There are a lot of details. It didn't work for me out of the box, I had to install some packages, which I had to guess. You have always to check which operating system / graphics driver is needed. In my case I had to use Ubuntu 19.10. Note this distro uses a very outdated exiv2-version as discussed in another thread here. Al ____________________________________________________________________________ darktable user mailing list to unsubscribe send a mail to [email protected]
