Re: CUDA Sorting

On Feb 13, 2012 11:39 a.m., "Kohei KaiGai" <kaigai(at)kaigai(dot)gr(dot)jp> wrote:
>
> 2012/2/13 Greg Smith <greg(at)2ndquadrant(dot)com>:
> > On 02/11/2012 08:14 PM, Gaetano Mendola wrote:
> >>
> >> 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.
> >
> >
> > The C410X adds 16 PCIe slots to a server, housed inside a separate 3U
> > enclosure. That's a completely sensible purchase if your goal is to
build a
> > computing cluster, where a lot of work is handed off to a set of GPUs.
I
> > think that's even less likely to be a cost-effective option for a
database
> > server. Adding a single dedicated GPU installed in a server to
accelerate
> > sorting is something that might be justifiable, based on your
benchmarks.
> > This is a much more expensive option than that though. Details at
> > http://www.dell.com/us/enterprise/p/poweredge-c410x/pd for anyone who
wants
> > to see just how big this external box is.
> >
> >
> >> 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
> >
> >
> > That's really interesting, and the X5650 is by no means a slow CPU. So
this
> > benchmark is providing a lot of CPU power yet still seeing over a 6X
speedup
> > in sort times. It sounds like the PCI Express bus has gotten fast
enough
> > that the time to hand data over and get it back again can easily be
> > justified for medium to large sized sorts.
> >
> > It would be helpful to take this patch and confirm whether it scales
when
> > using in parallel. Easiest way to do that would be to use the pgbench
"-f"
> > feature, which allows running an arbitrary number of some query at once.
> > Seeing whether this acceleration continued to hold as the number of
clients
> > increases is a useful data point.
> >
> > Is it possible for you to break down where the time is being spent? For
> > example, how much of this time is consumed in the GPU itself, compared
to
> > time spent transferring data between CPU and GPU? I'm also curious
where
> > the bottleneck is at with this approach. If it's the speed of the
PCI-E bus
> > for smaller data sets, adding more GPUs may never be practical. If the
bus
> > can handle quite a few of these at once before it saturates, it might be
> > possible to overload a single GPU. That seems like it would be really
hard
> > to reach for database sorting though; I can't really defend justify my
gut
> > feel for that being true though.
> >
> >
> >> > 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
> >
> >
> > Yes, and "640K will be enough for everyone", ha ha. (Having said the
640K
> > thing is flat out denied by Gates, BTW, and no one has come up with
proof
> > otherwise).
> >
> > I think you've made an interesting case for this sort of acceleration
now
> > being useful for systems doing what's typically considered a data
warehouse
> > task. I regularly see servers waiting for far more than 13M integers to
> > sort. And I am seeing a clear trend toward providing more PCI-E slots
in
> > servers now. Dell's R810 is the most popular single server model my
> > customers have deployed in the last year, and it has 5 X8 slots in it.
It's
> > rare all 5 of those are filled. As long as a dedicated GPU works fine
when
> > dropped to X8 speeds, I know a fair number of systems where one of those
> > could be added now.
> >
> > There's another data point in your favor I didn't notice before your
last
> > e-mail. Amazon has a "Cluster GPU Quadruple Extra Large" node type that
> > runs with NVIDIA Tesla hardware. That means the installed base of
people
> > who could consider CUDA is higher than I expected. To demonstrate how
much
> > that costs, to provision a GPU enabled reserved instance from Amazon
for one
> > year costs $2410 at "Light Utilization", giving a system with 22GB of
RAM
> > and 1.69GB of storage. (I find the reserved prices easier to compare
with
> > dedicated hardware than the hourly ones) That's halfway between the
> > High-Memory Double Extra Large Instance (34GB RAM/850GB disk) at $1100
and
> > the High-Memory Quadruple Extra Large Instance (64GB RAM/1690GB disk) at
> > $2200. If someone could prove sorting was a bottleneck on their server,
> > that isn't an unreasonable option to consider on a cloud-based database
> > deployment.
> >
> > I still think that an approach based on OpenCL is more likely to be
suitable
> > for PostgreSQL, which was part of why I gave CUDA low odds here. The
points
> > in favor of OpenCL are:
> >
> > -Since you last posted, OpenCL compiling has switched to using LLVM as
their
> > standard compiler. Good PostgreSQL support for LLVM isn't far away. It
> > looks to me like the compiler situation for CUDA requires their
PathScale
> > based compiler. I don't know enough about this area to say which
compiling
> > tool chain will end up being easier to deal with.
> >
> > -Intel is making GPU support standard for OpenCL, as I mentioned before.
> > NVIDIA will be hard pressed to compete with Intel for GPU acceleration
once
> > more systems supporting that enter the market.
> >
> > -Easy availability of OpenCL on Mac OS X for development sake. Lots of
> > Postgres hackers with OS X systems, even though there aren't too many
OS X
> > database servers.
> >
> > The fact that Amazon provides a way to crack the chicken/egg hardware
> > problem immediately helps a lot though, I don't even need a physical
card
> > here to test CUDA GPU acceleration on Linux now. With that data point,
your
> > benchmarks are good enough to say I'd be willing to help review a patch
in
> > this area here as part of the 9.3 development cycle. That may validate
that
> > GPU acceleration is useful, and then the next step would be considering
how
> > portable that will be to other GPU interfaces. I still expect CUDA
will be
> > looked back on as a dead end for GPU accelerated computing one day.
> > Computing history is not filled with many single-vendor standards who
> > competed successfully against Intel providing the same thing. AMD's
x86-64
> > is the only example I can think of where Intel didn't win that sort of
race,
> > which happened (IMHO) only because Intel's Itanium failed to prioritize
> > backwards compatibility highly enough.
> >
> As a side node. My module (PG-Strom) also uses CUDA, although it tried to
> implement it with OpenCL at begining of the project, because it didn't
work
> well when multiple sessions uses a GPU device concurrently.
> The second background process get an error due to out-of-resources during
> another process opens a GPU device.
>
> I'm not clear whether it is a limitation of OpenCL, driver of Nvidia, or
bugs of
> my code. Anyway, I switched to CUDA, instead of the investigation on
binary
> drivers. :-(
>
> Thanks,
> --
> KaiGai Kohei <kaigai(at)kaigai(dot)gr(dot)jp>

I have no experience with opencl but for sure with Cuda4.1 you can share
the same device from multiple host thread, as in for example allocate
memory in one host thread and use it in another thread. May be with opencl
you were facing the very same limit.