multi-gpu

Are all displays returned from .NET's Screen.AllScreens regardless of hardware configuration? For example, on a single PC you can have: Video card out to two display = total 2 displays Video cards each out to 1 display = total 2 displays, Video cards each out to 2 displays = 6 displays, Eyefinity card out to 6 displays (on displayports) In all these cases, if I use Screen.AllScreens can I access each display individually? Also, what if I have a card in extended mode, meaning 2 displays plugged into one card but it's just one big desktop (what I use at work)? Can I still specify content to

I have a server with multiple GPUs and want to make full use of them during model inference inside a java app. By default tensorflow seizes all available GPUs, but uses only the first one. I can think of three options to overcome this issue: Restrict device visibility on process level, namely using CUDA_VISIBLE_DEVICES environment variable. That would require me to run several instances of the java app and distribute traffic among them. Not that tempting idea. Launch several sessions inside a single application and try to assign one device to each of them via ConfigProto : public class

Currently I'm work with two gtx 650 . My program resembles in simple Clients/Server structure. I distribute the work threads on the two gpus. The Server thread need to gather the result vectors from client threads, so I need to copy the memory between the two gpu. Unfortunaly, the simple P2P program in cuda samples just doesn't work because my cards don't have TCC drivers. Spending two hours searching on google and SO, I can't find the answer.Some source says I should use cudaMemcpyPeer , and some other source says I should use cudaMemcpy with cudaMemcpyDefault .Is there some simple way to get

Would using multi-GPUs in Vulkan be something like making many command queues then dividing command buffers between them? There are 2 problems: In OpenGL, we use GLEW to get functions. With more than 1 GPU, each GPU has its own driver. How'd we use Vulkan? Would part of the frame be generated with a GPU & the others with other GPUs like use Intel GPU to render UI & AMD or Nvidia GPU to render game screen in labtops for example? Or would a frame be generated in a GPU & the next frame in an another GPU? Updated with more recent information, now that Vulkan exists. There are two kinds of multi

I'd like to know the possible ways to implement batch normalization layers with synchronizing batch statistics when training with multi-GPU. Caffe Maybe there are some variants of caffe that could do, like link . But for BN layer, my understanding is that it still synchronizes only the outputs of layers, not the means and vars. Maybe MPI can synchronizes means and vars but I think MPI is a little difficult to implemnt. Torch I've seen some comments here and here , which show the running_mean and running_var can be synchronized but I think batch mean and batch var can not or are difficult to

I have a standard tensorflow Estimator with some model and want to run it on multiple GPUs instead of just one. How can this be done using data parallelism? I searched the Tensorflow Docs but did not find an example; only sentences saying that it would be easy with Estimator. Does anybody have a good example using the tf.learn.Estimator? Or a link to a tutorial or so? I think tf.contrib.estimator.replicate_model_fn is a cleaner solution. The following is from tf.contrib.estimator.replicate_model_fn documentation, ... def model_fn(...): # See `model_fn` in `Estimator`. loss = ... optimizer = tf