In databases, processing large tables is a central
issue, and indeed
there is a considerable work on partitioning table operations
between processors. The ideas of conditioning the data is applicable
and was carried out.
Pointers to some of this work can start with:
"Evaluating Production systems in a multiprocessor
environment" By Cohen and Dechter. Appears in
"Artificial Intelligence and Computer Vision, Feldman and Brucksten
(Ed.), Elsevier Science Publishers, 1991.
The paper is not available online but I will be happy
to send a copy.
-----Rina.
>Anders L Madsen wrote:
>> Hi All,
>>
>> As has been mentioned by many researchers, a good way of exploiting
>> parallelisme in multiprocessor computers when performing Bayesian
>> inference is to have the table operations split up in independent
>> subtasks.
>>
>> Does anyone know of any algorithms developed for performing
>> multiplication, division, and marginalization of multiple dimensioned
>> probability potentials or more general multiple dimensioned structures?
>>
>> Any replies will be appreciated.
>>
>
>Parallelization of probabilistic inference was part of my thesis. You
>can find it at:
>
>http://robotics.Stanford.EDU/~alexvk/Public/thesis.ps
>
>The relevant parts were also published at the IEEE journal (although
>the thesis version is more complete):
>
>IEEE Computer, Dec. 1996, vol.29, no. 12, pp. 33-40.
>
>You can also find the paper online at:
>
>http://robotics.Stanford.EDU/~alexvk/Public/Papers/parallel-ieee-96.ps
>
>In the thesis I show that, given care of data locality---which is an
>important factor to achieve a good speedups on a multiprocessor---
>parallelization of operations on potentials and global conditioning
>are very similar in spirit (different in results). One can consider
>parallelization of message propagation as an optimization of global
>conditioning (we optimize out some redundant computations this way).
>
>I'll be happy to hear any development/criticism of this work.
>
>--
>Alexander V. Kozlov | alexvk@engr.sgi.com | (650) 933-8493