On 12/02/2012 13:13, Oleg Bartunov wrote:
I'm wondering if CUDA will win in geomentry operations, for example,
tesing point <@ complex_polygon
I'm not sure if the algorithm you mentioned can be implemented in terms
of vector algebra, blas, etc.
It's plenty of geometry operations implemented in CUDA out there, my
field of CUDA application is not this one so I'm not that much in it.
However I can point you to official NVIDIA npp library that provides
vector algebra algorithms, and some geometry algorithms as well.
http://developer.download.nvidia.com/compute/DevZone/docs/html/CUDALibraries/doc/NPP_Library.pdf
(take a look at around page 620).
Regards
Gaetano Mendola
Oleg
On Sun, 12 Feb 2012, Gaetano Mendola wrote:
On 19/09/2011 16:36, Greg Smith wrote:
On 09/19/2011 10:12 AM, Greg Stark wrote:
With the GPU I'm curious to see how well
it handles multiple processes contending for resources, it might be a
flashy feature that gets lots of attention but might not really be
very useful in practice. But it would be very interesting to see.
The main problem here is that the sort of hardware commonly used for
production database servers doesn't have any serious enough GPU to
support CUDA/OpenCL available. The very clear trend now is that all
systems other than gaming ones ship with motherboard graphics chipsets
more than powerful enough for any task but that. I just checked the 5
most popular configurations of server I see my customers deploy
PostgreSQL onto (a mix of Dell and HP units), and you don't get a
serious GPU from any of them.
Intel's next generation Ivy Bridge chipset, expected for the spring of
2012, is going to add support for OpenCL to the built-in motherboard
GPU. We may eventually see that trickle into the server hardware side of
things too.
The trend is to have server capable of running CUDA providing GPU via
external hardware (PCI Express interface with PCI Express switches),
look for example at PowerEdge C410x PCIe Expansion Chassis from DELL.
I did some experimenst timing the sort done with CUDA and the sort
done with pg_qsort:
CUDA pg_qsort
33Milion integers: ~ 900 ms, ~ 6000 ms
1Milion integers: ~ 21 ms, ~ 162 ms
100k integers: ~ 2 ms, ~ 13 ms
CUDA time has already in the copy operations (host->device,
device->host).
As GPU I was using a C2050, and the CPU doing the pg_qsort was a
Intel(R) Xeon(R) CPU X5650 @ 2.67GHz
Copy operations and kernel runs (the sort for instance) can run in
parallel, so while you are sorting a batch of data, you can copy the
next batch in parallel.
As you can see the boost is not negligible.
Next Nvidia hardware (Keplero family) is PCI Express 3 ready, so
expect in the near future the "bottle neck" of the
device->host->device copies to have less impact.
I strongly believe there is space to provide modern database engine of
a way to offload sorts to GPU.
I've never seen a PostgreSQL server capable of running CUDA, and I
don't expect that to change.
That sounds like:
"I think there is a world market for maybe five computers."
- IBM Chairman Thomas Watson, 1943
Regards
Gaetano Mendola
Regards,
Oleg
_____________________________________________________________
Oleg Bartunov, Research Scientist, Head of AstroNet (www.astronet.ru),
Sternberg Astronomical Institute, Moscow University, Russia
Internet: o...@sai.msu.su, http://www.sai.msu.su/~megera/
phone: +007(495)939-16-83, +007(495)939-23-83
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