There are two parts to that answer (I wrote it). One part is easy to quantify, the other is more empirical. Hardware Constraints: This is the easy to quantify part. Appendix F of the current CUDA programming guide lists a number of hard limits which limit how many threads per block a kernel launch can … Read more
[Edit OCT 2017 as even this answer gets quite old] Most of these answers are quite old, so I thought I’d give an updated summary of where I think each project is: GPU.Net (TidePowerd) – I tried this 6 months ago or so, and did get it working though it took a little bit of … Read more
First of all, you should be aware of the fact that CUDA will not automagically make computations faster. On the one hand, because GPU programming is an art, and it can be very, very challenging to get it right. On the other hand, because GPUs are well-suited only for certain kinds of computations. This may … Read more
Check out CUDA by NVidia, IMO it’s the easiest platform to do GPU programming. There are tons of cool materials to read. http://www.nvidia.com/object/cuda_home.html Hello world would be to do any kind of calculation using GPU. Hope that helps.
There is an undocumented method called device_lib.list_local_devices() that enables you to list the devices available in the local process. (N.B. As an undocumented method, this is subject to backwards incompatible changes.) The function returns a list of DeviceAttributes protocol buffer objects. You can extract a list of string device names for the GPU devices as … Read more
If I understand correctly, performance-wise, flipping the sign of Z and the depth test is nothing but changing a < comparison to a > comparison. So, if I understand correctly and the author isn’t lying or making things up, then changing < to > used to be a vital optimization for many games. I didn’t … Read more
These functions should help: >>> import torch >>> torch.cuda.is_available() True >>> torch.cuda.device_count() 1 >>> torch.cuda.current_device() 0 >>> torch.cuda.device(0) <torch.cuda.device at 0x7efce0b03be0> >>> torch.cuda.get_device_name(0) ‘GeForce GTX 950M’ This tells us: CUDA is available and can be used by one device. Device 0 refers to the GPU GeForce GTX 950M, and it is currently chosen by PyTorch.
No, I don’t think “open CUDA library” is enough to tell, because different nodes of the graph may be on different devices. When using tensorflow2: print(“Num GPUs Available: “, len(tf.config.list_physical_devices(‘GPU’))) For tensorflow1, to find out which device is used, you can enable log device placement like this: sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) Check your console for this … Read more
Surprise surprise, rebooting solved the issue (I thought I had already tried that). The solution Robert Crovella mentioned in the comments may also be useful to someone else, since it’s pretty similar to what I did to solve the issue the first time I had it.