On 19/09/2011 21:41, PostgreSQL - Hans-Jürgen Schönig wrote:
On Sep 19, 2011, at 5:16 PM, Tom Lane wrote:
Greg Stark<st...@mit.edu> writes:
That said, to help in the case I described you would have to implement
the tapesort algorithm on the GPU as well.
I think the real problem would be that we are seldom sorting just the
key values. If you have to push the tuples through the GPU too, your
savings are going to go up in smoke pretty quickly …
i would argument along a similar line.
to make GPU code fast it has to be pretty much tailored to do exactly one thing
- otherwise you have no chance to get anywhere close to card-bandwith.
if you look at "two similar" GPU codes which seem to do the same thing you
might easily see that one is 10 times faster than the other - for bloody reason such as
memory alignment, memory transaction size or whatever.
this opens a bit of a problem: PostgreSQL sorting is so generic and so flexible
that i would be really surprised if somebody could come up with a solution
which really comes close to what the GPU can do.
it would definitely be interesting to see a prototype, however.
Thrust Nvidia library provides the same sorting flexibility as postgres
does.
// generate 32M random numbers on the host
thrust::host_vector<int> h_vec(32 << 20);
thrust::generate(h_vec.begin(), h_vec.end(), rand);
// transfer data to the device
thrust::device_vector<int> d_vec = h_vec;
// sort data on the device (846M keys per second on GeForce GTX 480)
thrust::sort(d_vec.begin(), d_vec.end());
// transfer data back to host
thrust::copy(d_vec.begin(), d_vec.end(), h_vec.begin());
as you can see the type to be ordered is template, and
the thrust::sort have also a version in where it takes the comparator to
use.
So compared with pg_qsort thrust::sort gives you the same flexibility.
http://docs.thrust.googlecode.com/hg/group__sorting.html
Regards
Gaetano Mendola
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