On 3/21/07, Gary S. Thompson <garyt@xxxxxxxxxxxxxxx> wrote:
Edward d'Auvergne wrote:
> My personal experience with the coding of the grid computing code, I
> would assume that there are a number of differences. For example the
> algorithm relax currently uses to handle computers of different
> speeds.
This should not make a difference you just divide more finely and weight
the size of the job by computer (you can even send off the finer grained
task one at a time and then send new ones as tasks are completed... the
same way as you do in the thread code)
The model-free problem places a lower bound on the minimum granularity
size. That limit is the optimisation instance. For almost all
minimisation techniques (the grid search excluded) parallelisation is
not possible. Say you have a calculation which would take 20 min on a
3 GHz machine but it is sent to a 500 MHz old clunker in the basement.
These are serious performance issues in a grid that add up - they
need to be handled properly and not solely by making the calculations
more fine grained. And a couple of those old machines in addition to
the fast ones could significantly speed up model-free analysis.
> Grid computing is designed for a heterogeneous environment
> whereas MPI is not. I'm not saying one is superior to the other but
> that they have different applications in different computing
> environments.
mpi can cope with a hetrogeneous environment as explained above
Not well though. And a likely scenario - a Windows user turning off
their machine when they go home rather than just logging out - is not
something MPI is designed for. It is a tool which can be fitted into
these situations, but in this case it isn't the best tool for the job.
Grid computing is the best tool. In the situation of a roughly
homogeneous fault-free environment or a cluster, grid computing is not
the best tool, MPI is.
Cheers,
Edward
Re: r3237 - in /branches/multi_processor: multi/mpi4py_processor.py relax