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Re: CUDA Sorting

From: Vitor Reus <vitor(dot)reus(at)gmail(dot)com>
To: Hannu Krosing <hannu(at)krosing(dot)net>
Cc: Greg Smith <greg(at)2ndquadrant(dot)com>, pgsql-hackers(at)postgresql(dot)org
Subject: Re: CUDA Sorting
Date: 2011-09-27 12:56:25
Message-ID: CALf5ONp=8Dva=+GtB-8wQeHQWTMH6-2m7RcpcO5_iVB7TK4m2g@mail.gmail.com (view raw or flat)
Thread:
Lists: pgsql-hackers
Hey hackers,

I'm still having problems reading the values of the columns in tuplesort.c,
in order to understand how to port this to CUDA.

Should I use the heap_getattr macro to read them?

2011/9/24 Hannu Krosing <hannu(at)krosing(dot)net>

> On Mon, 2011-09-19 at 10:36 -0400, 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.
> >
> > I've never seen a PostgreSQL server capable of running CUDA, and I don't
> > expect that to change.
>
> CUDA sorting could be beneficial on general server hardware if it can
> run well on multiple cpus in parallel. GPU-s being in essence parallel
> processors on fast shared memory, it may be that even on ordinary RAM
> and lots of CPUs some CUDA algorithms are a significant win.
>
> and then there is non-graphics GPU availabe on EC2
>
>  Cluster GPU Quadruple Extra Large Instance
>
>  22 GB of memory
>  33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem”
>       architecture)
>  2 x NVIDIA Tesla “Fermi” M2050 GPUs
>  1690 GB of instance storage
>  64-bit platform
>  I/O Performance: Very High (10 Gigabit Ethernet)
>  API name: cg1.4xlarge
>
> It costs $2.10 per hour, probably a lot less if you use the Spot
> Instances.
>
> > --
> > Greg Smith   2ndQuadrant US    greg(at)2ndQuadrant(dot)com   Baltimore, MD
> > PostgreSQL Training, Services, and 24x7 Support  www.2ndQuadrant.us
> >
> >
>
>
>
> --
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