On Fri, 17 Apr 2015 16:53:56 -0500, James Carman wrote:
Do you have any pointers to code for this ForkJoin mechanism? I'm
curious to see it.
The key thing you will need in order to support parallelization in a
generic way
What do you mean by "generic way"?
I'm afraid that we may be trying to compare apples and oranges;
each of us probably has in mind a "prototype" algorithm and an idea
of how to implement it to make it run in parallel.
I think that it would focus the discussion if we could
1. tell what the "prototype" is,
2. show a sort of pseudo-code of the difference between a sequential
and a parallel run of this "prototype" (i.e. what is the data, how
the (sub)tasks operate on them).
Regards,
Gilles
is to not tie it directly to threads, but use some
abstraction layer above threads, since that may not be the "worker"
method you're using at the time.
On Fri, Apr 17, 2015 at 2:57 PM, Thomas Neidhart
<thomas.neidh...@gmail.com> wrote:
On 04/17/2015 05:35 PM, Phil Steitz wrote:
On 4/17/15 3:14 AM, Gilles wrote:
Hello.
On Thu, 16 Apr 2015 17:06:21 -0500, James Carman wrote:
Consider me poked!
So, the Java answer to "how do I run things in multiple threads"
is to
use an Executor (java.util). This doesn't necessarily mean that
you
*have* to use a separate thread (the implementation could execute
inline). However, in order to accommodate the separate thread
case,
you would need to code to a Future-like API. Now, I'm not saying
to
use Executors directly, but I'd provide some abstraction layer
above
them or in lieu of them, something like:
public interface ExecutorThingy {
Future<T> execute(Function<T> fn);
}
One could imagine implementing different ExecutorThingy
implementations which allow you to parallelize things in
different
ways (simple threads, JMS, Akka, etc, etc.)
I did not understand what is being suggested: parallelization of a
single algorithm or concurrent calls to multiple instances of an
algorithm?
Really both. It's probably best to look at some concrete examples.
The two I mentioned in my apachecon talk are:
1. Threads managed by some external process / application
gathering
statistics to be aggregated.
2. Allowing multiple threads to concurrently execute GA
transformations within the GeneticAlgorithm "evolve" method.
It would be instructive to think about how to handle both of these
use cases using something like what James is suggesting. What is
nice about his idea is that it could give us a way to let users /
systems decide whether they want to have [math] algorithms spawn
threads to execute concurrently or to allow an external execution
framework to handle task distribution across threads.
I since a more viable option is to take advantage of the ForkJoin
mechanism that we can use now in math 4.
For example, the GeneticAlgorithm could be quite easily changed to
use a
ForkJoinTask to perform each evolution, I will try to come up with
an
example soon as I plan to work on the genetics package anyway.
The idea outlined above sounds nice but it is very unclear how an
algorithm or function would perform its parallelization in such a
way,
and whether it would still be efficient.
Thomas
Since 2. above is a good example of "internal" parallelism and it
also has data sharing / transfer challenges, maybe its best to
start
with that one. I have just started thinking about this and would
love to get better ideas than my own hacking about how to do it
a) Using Spark with RDD's to maintain population state data
b) Hadoop with HDFS (or something else?)
Phil
Gilles
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