CODEWITHSUNDEEP
c++openglcuda
anshu
anshu

Reputation: 665

Running CUDA and OpenGL in parallel without using interoperability

I am building a real-time signal processing and display system using a nVidia Tesla C2050 GPU. The design was such that the signal processing part would run as a separate program and do all the computations using CUDA. In parallel, if needed I can start a separate display program which displays the processed signal using OpenGL.Since the design was to run the processes as independent processes, I do not have any CUDA-OpenGL interoperability These two programs exchange data with each other over a UNIX stream socket.

The signal processing program spends most of the time using the GPU for the CUDA stuff.I am refreshing my frame in OpenGL every 50 msecs while the CUDA program runs for roughly 700 msecs for each run and two sequential runs are usually separated by 30-40 msecs. When I run the programs one at a time (i.e. only CUDA or OpenGL part is running) everything works perfectly. But when I start the programs together, the display is also not what it is supposed to be, while the CUDA part produces the correct output. I have checked the socket implementation and I am fairly confident that the sockets are working correctly.

My question is since I have a single GPU and no CUDA-OpenGL interoperability and both the processes use the GPU regularly, is it possible that the context switching between the CUDA kernel and the OpenGL kernel is interfering with each other. Should I change the design to have a single program to run bot the parts with CUDA-OpenGL interoperability.

Upvotes: 2

Views: 1669

Answers (1)

Greg Smith
Greg Smith

Reputation: 11509

Compute capability 5.0 and less devices cannot run graphics and compute concurrently. The Tesla C2050 does not support any form of pre-emption so while the CUDA kernel is executing the GPU cannot be used to render the OpenGL commands. CUDA-OpenGL interop does not solve this issue.

If you have a single GPU then the best option is to break the CUDA kernels into shorter launches so that the GPU can switch between compute in graphics. In the aformentioned case the CUDA kernel should not execute for more than 50ms - GLRenderTime.

Using a second GPU to do the graphics rendering would be the better option.

Upvotes: 4

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