Using a GPU both as video card and GPGPU

Question

Where I work, we do a lot of numerical computations and we are considering buying workstations with NVIDIA video cards because of CUDA (to work with TensorFlow and Theano).

My question is: should these computers come with another video card to handle the display and free the NVIDIA for the GPGPU?

I would appreciate if anyone knows of hard data on using a video card for display and GPGPU at the same time.

Answer 1

Having been through this, I'll add my two cents.

It is helpful to have a dedicated card for computations, but it is definitely not necessary.

I have used a development workstation with a single high-end GPU for both display and compute. I have also used workstations with multiple GPUs, as well as headless compute servers.

My experience is that doing compute on the display GPU is fine as long as demands on the display are typical for software engineering. In a Linux setup with a couple monitors, web browsers, text editors, etc., I use about 200MB for display out of the 6GB of the card -- so only about 3% overhead. You might see the display stutter a bit during a web page refresh or something like that, but the throughput demands of the display are very small.

One technical issue worth noting for completeness is that the NVIDIA driver, GPU firmware, or OS may have a timeout for kernel completion on the display GPU (run NVIDIA's 'deviceQueryDrv' to see the driver's "run time limit on kernels" setting). In my experience (on Linux), with machine learning, this has never been a problem since the timeout is several seconds and, even with custom kernels, synchronization across multiprocessors constrains how much you can stuff into a single kernel launch. I would expect the typical runs of the pre-baked ops in TensorFlow to be two or more orders of magnitude below this limit.

That said, there are some big advantages of having multiple compute-capable cards in a workstation (whether or not one is used for display). Of course there is the potential for more throughput (if your software can use it). However, the main advantage in my experience, is being able to run long experiments while concurrently developing new experiments.

It is of course feasible to start with one card and then add one later, but make sure your motherboard has lots of room and your power supply can handle the load. If you decide to have two cards, with one being a low-end card dedicated to display, I would specifically advise against having the low-end card be a CUDA-capable card lest it get selected as a default for computation.

Hope that helps.

Answer 2

In my experience it is awkward to share a GPU card between numerical computation tasks and driving a video monitor. For example, there is limited memory available on any GPU, which is often the limiting factor in the size of a model you can train. Unless you're doing gaming, a fairly modest GPU is probably adequate to drive the video. But for serious ML work you will probably want a high-performance card. Where I work (Google) we typically put two GPUs in desk-side machines when one is to be used for numerical computation.