Re: Is offloading to GPU a worthwhile feature?

Konstantin Ryabitsev [off-list ref] writes:

On 04/08/18 09:59, Jakub Narebski wrote:

quoted

quoted

This is an entirely idle pondering kind of question, but I wanted to
ask. I recently discovered that some edge providers are starting to
offer systems with GPU cards in them -- primarily for clients that need
to provide streaming video content, I guess. As someone who needs to run
a distributed network of edge nodes for a fairly popular git server, I
wondered if git could at all benefit from utilizing a GPU card for
something like delta calculations or compression offload, or if benefits
would be negligible.

The problem is that you need to transfer the data from the main memory
(host memory) geared towards low-latency thanks to cache hierarchy, to
the GPU memory (device memory) geared towards bandwidth and parallel
access, and back again.  So to make sense the time for copying data plus
the time to perform calculations on GPU (and not all kinds of
computations can be speed up on GPU -- you need fine-grained massively
data-parallel task) must be less than time to perform calculations on
CPU (with multi-threading).

Would something like this be well-suited for tasks like routine fsck,
repacking and bitmap generation? That's the kind of workloads I was
imagining it would be most well-suited for.

All of those, I think, would need to use some graph algorithms.  While
there are here ready graph libraries on GPU (like nVidia's nvGRAPH),
graphs are irregular structures not that well souted to the SIMD type of
parallelism that GPU is best for.

I also wonder if the amound of memory on GPU would be enough (and if
not, would be it possible to perform calculations in batches).

quoted

Also you would need to keep non-GPU and GPGPU code in sync.  Some parts
of code do not change much; and there also solutions to generate dual
code from one source.

Still, it might be good idea,

I'm still totally the wrong person to be implementing this, but I do
have access to Packet.net's edge systems which carry powerful GPUs for
projects that might be needing these for video streaming services. It
seems a shame to have them sitting idle if I can offload some of the
RAM- and CPU-hungry tasks like repacking to be running there.

Happily, GPGPU programming (in CUDA C mainly, which limits use to nVidia
hardware) is one of my areas if interests...

Best regards,
--
Jakub Narębski