http://developer.nvidia.com/object/optix-home.html. 2020-04-21 01:02:10+0900: Cuda backend: Found GPU GeForce RTX 2060 memory 6442450944 compute capability major 7 minor 5 You signed in with another tab or window.

2020-04-21 01:02:10+0900: Cuda backend: Model version 8 useFP16 = true useNHWC = true Message boards: Number crunching: OpenCL vs CUDA (Stock) ©2020 University of California SETI@home and Astropulse are funded by grants from the National Science Foundation, NASA, and donations from SETI@home volunteers. That is, aside from seeing which has the better performance and simply picking the one that works better with their GPU (choosing between the two can be a big speed difference), fussing about if there is some other phantom difference between the two besides speed while ignoring the major impact of threads. numSearchThreads = 16: +169 Elo

:).

If interested see also other notes about performance and mem usage in the top of gtp.cfg.

Is the amount of work done in parallel approximately the same for the both approaches. GLSL doesn’t let you use shared RAM or synchronize threads, which probably means more global memory traffic. Posted: Fri Sep 25, 2020 6:21 pm .

In the Cuda app case, there are presently hard scaling limits (likely to be lifted later with optional CPU cost). to your account. Learn more, Difference between cuda version and opencl version.

I already know about performance in gaming benchmark, value, performance ratio, and etc. numSearchThreads = 32: 10 / 10 positions, visits/s = 1235.53 nnEvals/s = 1064.41 nnBatches/s = 55.36 avgBatchSize = 19.23 (24.5 secs). Yes, if CUDA is also using FP32, then the only difference is speed. Automatically trying different numbers of threads to home in on the best: 2020-04-21 01:02:12+0900: nnRandSeed0 = 12548376389706790299 where TIME is the number of seconds you want KataGo to be strongest for, such as the number of seconds you often wait for during analysis or in matches (this is used in the estimation of the negative impact of threads - high threads is most harmful for very short searches), For example, 5 or 10 seconds would be a good general-purpose number: We would like to express our deep gratitude to lightvector. Page 1 of 1 [ 2 posts ] Previous topic | Next topic : Author Message ; RobertJasiek Post subject: OptiX versus CUDA or OpenCL. 2020-04-21 01:02:10+0900: nnRandSeed0 = 8569346161061287098 You could always make the threads match, disable FP16, and then you lose all performance benefits, but now they will be equal at 1000 playouts again. It's because of 32 vs 128 threads. numSearchThreads = 40: 10 / 10 positions, visits/s = 1281.14 nnEvals/s = 1090.55 nnBatches/s = 41.86 avgBatchSize = 26.05 (23.7 secs) (EloDiff +196)

numSearchThreads = 20: 10 / 10 positions, visits/s = 1133.26 nnEvals/s = 953.53 nnBatches/s = 95.20 avgBatchSize = 10.02 (26.6 secs) (EloDiff +187) Nvidia's OpenCL driver is really just a wrapper over CUDA anyway since it translates OpenCL calls into CUDA calls. ./katago benchmark -model YOUR_MODEL.bin.gz -config YOUR_CONFIG.cfg -tune -time 5 -v 3000. I'm from eggxpert in chat room/forums.

This is more or less how I explained it to myself.

Thanks a lot!

numSearchThreads = 48: 10 / 10 positions, visits/s = 1286.36 nnEvals/s = 1127.92 nnBatches/s = 36.14 avgBatchSize = 31.21 (23.7 secs) numSearchThreads = 5: (baseline) The two of them compute identical values out to the 7th or 8th decimal place.

This is not because of any difference between OpenCL and CUDA, and and mostly not because of FP16 either.

Else, nnMaxBatchSize = 132

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On most GPUs if you only have FP32, then OpenCL is faster so no reason to use CUDA. On a 64-bit platform try compiling the CUDA application as a 32-bit application. An example can be found here. No, the memory traffic can be much bigger.

Optimal number of threads is fairly high, tripling the search limit and trying again. Successfully merging a pull request may close this issue.

After some surfing on the web and some thinking I have identified two hardware-accelerated ways to do this: Alternative #1 is truely parallel, thus I believe it would help me to make the most of the modern GPUs. We use essential cookies to perform essential website functions, e.g. 2020-04-21 01:02:13+0900: Cuda backend: Found GPU GeForce RTX 2060 memory 6442450944 compute capability major 7 minor 5

On request, using the same data I collected for my most. 2020-04-21 01:02:10+0900: Cuda backend: Model name: g170-b20c256x2-s3761649408-d809581368, 2020-04-21 01:02:12+0900: Loaded config gtp.cfg We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

2020-04-21 01:02:13+0900: Cuda backend: Model version 8 useFP16 = true useNHWC = true numSearchThreads = 60: 10 / 10 positions, visits/s = 1224.52 nnEvals/s = 1181.04 nnBatches/s = 34.21 avgBatchSize = 34.52 (7.0 secs) (EloDiff -35)

Implementation based on OpenGL SL. numSearchThreads = 64: 10 / 10 positions, visits/s = 1313.92 nnEvals/s = 1149.19 nnBatches/s = 29.86 avgBatchSize = 38.49 (23.3 secs) (EloDiff +161), Based on some test data, each speed doubling gains perhaps ~250 Elo by searching deeper. Presumably the reason you use 128 threads with FP16 (or whatever the benchmark tool tells you is optimal threads) is because 128 threads gets you better performance - the 1000 playouts would much faster so if you were using fixed time instead, you could get much more than 1000 in the same time, so it would be stronger, but fixed at only 1000 it will be weaker. You can find some results by looking through the Inconclusive list, You can run more than 1 per GPU with stock by just adding an app_config.xml, http://setiathome.berkeley.edu/workunit.php?wuid=2237103948, http://setiathome.berkeley.edu/result.php?resultid=5097390926, http://setiathome.berkeley.edu/result.php?resultid=5064040827, https://setisvn.ssl.berkeley.edu/trac/browser/branches/sah_v7_opt/Xbranch/client/alpha/PetriR_raw3. 2020-04-21 01:02:13+0900: Cuda backend: Model name: g170-b20c256x2-s3761649408-d809581368.

In other words, if you set an upper limit on the number of searches and set the time to exhaust the number of searches, will both be the same strength? NVIDIA OpenCL implementation is 32-bit and doesn't conform to the same function call requirements as CUDA. Example: if you have RTX2080 and you do 1000 playouts OpenCL with 32 threads against 1000 playouts CUDA FP16 with 128 threads, for example, then the OpenCL will win more often. The katago-cuda benchmark test looks like this. nnMutexPoolSizePowerOfTwo = 16. One other major detail: for any fixed number of visits or playouts, the more threads you use, the weaker the strength. Already on GitHub? :).

It is currently Wed Oct 28, 2020 1:13 am: Board index » Go Gear » Computer Go. numSearchThreads = 48: 10 / 10 positions, visits/s = 1286.36 nnEvals/s = 1127.92 nnBatches/s = 36.14 avgBatchSize = 31.21 (23.7 secs) (EloDiff +182) From this result, 32 threads seems to be the most balanced number, so I will apply 32 threads.

numSearchThreads = 16: 10 / 10 positions, visits/s = 1010.92 nnEvals/s = 911.44 nnBatches/s = 115.60 avgBatchSize = 7.88 (8.1 secs) (EloDiff +488) AstroPulse is funded in … Sorry for making it hard to see.

I guess it might be, as calculatiins are indepndent for each pixel….

2020-04-21 01:05:53+0900: After dedups: nnModelFile0 = 20x256.bin.gz useFP16 auto useNHWC auto

If you intend to do much longer searches, configure the seconds per game move you expect with the '-time' flag and benchmark again. 2020-04-21 01:05:54+0900: Cuda backend: Model version 8 useFP16 = true useNHWC = true

This discussion is about OpenCL vs Cuda for CS6 programs and general for PS, video-editing and 3D rendering.

2020-04-21 01:02:12+0900: After dedups: nnModelFile0 = 20x256.bin.gz useFP16 auto useNHWC auto numSearchThreads = 32: 10 / 10 positions, visits/s = 1235.53 nnEvals/s = 1064.41 nnBatches/s = 55.36 avgBatchSize = 19.23 (24.5 secs) (EloDiff +197) numSearchThreads = 48: +182 Elo

Threads larger than 132 are similar numbers so I usually use 132. ./katago benchmark -model YOUR_MODEL.bin.gz -config YOUR_CONFIG.cfg -tune -time TIME -v 3000

I somehow expected this; Latest GPUs are not used well by now old Cuda 5.0, while OpenCL is a bit higher level programming and OpenCL driver itself is doing a better job optimizing the task to actual higher-end GPU hardware. CUDA is much faster than OpenCL. We sincerely apologize to the developers for making this connection.

numSearchThreads = 40: +196 Elo Is there anything you need to improve regarding strength? Is it correct to say that number of CUDA cores for some GPU is the same as number of shader processors? numSearchThreads = 16: 10 / 10 positions, visits/s = 1058.94 nnEvals/s = 902.96 nnBatches/s = 113.41 avgBatchSize = 7.96 (28.5 secs) (EloDiff +169) numSearchThreads = 132: 10 / 10 positions, visits/s = 1233.60 nnEvals/s = 1219.56 nnBatches/s = 21.33 avgBatchSize = 57.17 (7.5 secs) (EloDiff -176).

numSearchThreads = 32: +197 Elo (recommended) If CUDA is using FP16 tensor cores (tensor cores can be used on RTX 2080 or similar super-top nvidia gpus) then it will be roughly a doubling of speed compared to CUDA FP32. CUDA runtime applications compile the kernel code to have the same bitness as the application. Then since it's only using 16-bit floats, the neural net evaluations and policy will be a bit more noisy since they have lower precision.

May 6, 2020, 9:48pm #1. He had to run a shader just to generate the x & y coordinates for each pixel […]. Here’s the link GPU connected component labeling.

36 or 48 has almost the same performance, but will be stronger due to using fewer threads. numSearchThreads = 10: +115 Elo

All times are UTC . This is why you should always report the number of threads you use in any test - it affects the strength even if visits or playouts are constant. numSearchThreads = 64: +161 Elo, If you care about performance, you may want to edit numSearchThreads in gtp.cfg based on the above results! numSearchThreads = 6: +50 Elo No prob, hope that helped.

Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. numSearchThreads = 6: 10 / 10 positions, visits/s = 722.72 nnEvals/s = 614.44 nnBatches/s = 205.46 avgBatchSize = 2.99 (41.6 secs) (EloDiff +50)

In the benchmark test, if the numbers of visits / s are minute, the smaller the number of threads, the stronger. This is especially true if you need a feature that CUDA directly exposes to the developer, like shared memory.

This is partially because it’s inevitable (CUDA is designed by Nvidia for Nvidia hardware) and partially because Nvidia’s drivers for OpenCL have been historically terrible. WARNING: Your nnMaxBatchSize is hardcoded to 16, recommend deleting it and using the default (which this benchmark assumes) privacy statement. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. numSearchThreads = 12: 10 / 10 positions, visits/s = 977.72 nnEvals/s = 829.95 nnBatches/s = 139.11 avgBatchSize = 5.97 (30.8 secs) (EloDiff +148)

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