Multi GPU programming using texture memory in CUDA | CUDA multi-gpu textures
Those of you who have used CUDA texture should be familiar with
the insane programming interface that they present. Texture references can be
declared only as global variables, and only in the file where you are using
them. You can't pass them around as by - value/by –reference arguments in
functions, you can't create them inside function/class scope, and you cannot
make array s of them. This brings us to an interesting point. The way Multi-GPU
programming is handled in CUDA is that you spawn off many CPU threads. Each CPU
thread initializes its own GPU. For each thread, you create thread specific variables
that store pointers to the device memory, and allocate device memory in EACH
thread. That's right, since memory is not shared between GPUs, if you are doing
the same thing on many GPUs, you need to allocate the same stuff again and
again, once on each GPU (of course, if you are doing Different things on
different GPUs, that’s a different ball game). Now comes the fun part. Since
you cannot create array s of Texture References, and you cannot encapsulate
them inside a class/structure, how on earth can you create one Texture
Reference for each GPU? Should we hard code this? Should we replicate our code
for each GPU we have, and just change the name of the Texture Reference to some
unique name? The answer is NO!!
Well, it turns out that NVidia has sneakily done something here.
When we spawn off multiple CPU threads, and select a different GPU in each thread,
CUDA "auto magically” creates a separate copy of the texture reference for
each GPU. So, all you have to do is bind the SAME Texture reference again and
again, once in each CPU thread (i.e.: once for each GPU). While it may look
weird, because it looks like we are initializing the same(?) global variable
several times, this actually works.
I have uploaded a small code sample here that demonstrates this
stuff. It loads a bunch of numbers onto the GPUs and squares them. This code sample
uses some helper classes that I wrote to simplify CUDA development. This is
still ongoing work, and may not be as good as a
Framework/library should be. Also, you will need Boost C++
libraries (the cpu threading and synchronization
uses boost).
I hope you must like this article
and have learned CUDA multi-gpu textures
Got Questions?
Feel free to ask me any
question because I'd be happy to walk you through step by step!
Want to Contact us? Click here
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