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digitalmars.D.learn - Advice on threading/fibers/?

reply Justin Whear <justin economicmodeling.com> writes:
Consider the following:

You have 10 million data points and you need to apply a multipass algorithm to
them. Each pass is like a cellular
automata: it can read from the previous pass but it doesn't know the "current"
values. This makes the actual
processing of each value trivially parallelizable. The actual operation for
each value is fairly simple and cheap
(essentially a multidimensional ancestor-child averaging operation).

After each value has been operated on once, the pass is complete and the
"current" and "old" buffers are switched
(conceptually, the "current" buffer can only be written to, the "old" buffer
can only be read--using __gshared
here).

The number of passes is not fixed; in the course of each value operation, an
error is computed. When the worst
individual error falls below a certain threshold, the algorithm is finished.
Generally this will take between one
thousand and ten thousand passes.

How would you go about parallelizing this? My thought is to take the map/reduce
approach within each pass: each
thread/fiber takes a slice of the dataset, makes its modifications, then
returns an error summary. These summaries
are quickly combined and the algorithm loop decides whether to run again. Each
pass shouldn't take more than a
second or two, so I'm not sure whether introducing the overhead of spawning,
say, 10 threads each pass is
worthwhile (times 5000 passes). On the other hand, I have plenty of CPUs to
throw at it (at least 16 cores, each
with hyperthreading) and am in a situation where "as fast as possible" is
important (while individual datasets may
not grow, the number of them is).

Any thoughts appreciated.

Justin
Jun 15 2011
parent reply "Lars T. Kyllingstad" <public kyllingen.NOSPAMnet> writes:
On Wed, 15 Jun 2011 23:57:25 +0000, Justin Whear wrote:

 Consider the following:
 
 You have 10 million data points and you need to apply a multipass
 algorithm to them. Each pass is like a cellular automata: it can read
 from the previous pass but it doesn't know the "current" values. This
 makes the actual processing of each value trivially parallelizable. The
 actual operation for each value is fairly simple and cheap (essentially
 a multidimensional ancestor-child averaging operation).
 
 After each value has been operated on once, the pass is complete and the
 "current" and "old" buffers are switched (conceptually, the "current"
 buffer can only be written to, the "old" buffer can only be read--using
 __gshared here).
 
 The number of passes is not fixed; in the course of each value
 operation, an error is computed. When the worst individual error falls
 below a certain threshold, the algorithm is finished. Generally this
 will take between one thousand and ten thousand passes.
 
 How would you go about parallelizing this? My thought is to take the
 map/reduce approach within each pass: each thread/fiber takes a slice of
 the dataset, makes its modifications, then returns an error summary.
 These summaries are quickly combined and the algorithm loop decides
 whether to run again. Each pass shouldn't take more than a second or
 two, so I'm not sure whether introducing the overhead of spawning, say,
 10 threads each pass is worthwhile (times 5000 passes). On the other
 hand, I have plenty of CPUs to throw at it (at least 16 cores, each with
 hyperthreading) and am in a situation where "as fast as possible" is
 important (while individual datasets may not grow, the number of them
 is).
 
 Any thoughts appreciated.

I would recommend you take a look at the new std.parallelism module, which was introduced in the most recent DMD release (2.053): http://www.d-programming-language.org/phobos-prerelease/ std_parallelism.html -Lars
Jun 16 2011
next sibling parent "Lars T. Kyllingstad" <public kyllingen.NOSPAMnet> writes:
On Thu, 16 Jun 2011 13:22:33 +0000, Lars T. Kyllingstad wrote:

 On Wed, 15 Jun 2011 23:57:25 +0000, Justin Whear wrote:
 
 Any thoughts appreciated.

I would recommend you take a look at the new std.parallelism module, which was introduced in the most recent DMD release (2.053): http://www.d-programming-language.org/phobos-prerelease/ std_parallelism.html

Specifically, check out TaskPool.parallel(), TaskPool.amap(), TaskPool.map () and TaskPool.reduce(). My guess is one or more of these will be exactly what you need. -Lars
Jun 16 2011
prev sibling parent Jeremy Wright <jeremy quiescent.us> writes:
On 06/16/2011 06:22 AM, Lars T. Kyllingstad wrote:
 On Wed, 15 Jun 2011 23:57:25 +0000, Justin Whear wrote:

 Consider the following:

 You have 10 million data points and you need to apply a multipass
 algorithm to them. Each pass is like a cellular automata: it can read
 from the previous pass but it doesn't know the "current" values. This
 makes the actual processing of each value trivially parallelizable. The
 actual operation for each value is fairly simple and cheap (essentially
 a multidimensional ancestor-child averaging operation).

 After each value has been operated on once, the pass is complete and the
 "current" and "old" buffers are switched (conceptually, the "current"
 buffer can only be written to, the "old" buffer can only be read--using
 __gshared here).

 The number of passes is not fixed; in the course of each value
 operation, an error is computed. When the worst individual error falls
 below a certain threshold, the algorithm is finished. Generally this
 will take between one thousand and ten thousand passes.

 How would you go about parallelizing this? My thought is to take the
 map/reduce approach within each pass: each thread/fiber takes a slice of
 the dataset, makes its modifications, then returns an error summary.
 These summaries are quickly combined and the algorithm loop decides
 whether to run again. Each pass shouldn't take more than a second or
 two, so I'm not sure whether introducing the overhead of spawning, say,
 10 threads each pass is worthwhile (times 5000 passes). On the other
 hand, I have plenty of CPUs to throw at it (at least 16 cores, each with
 hyperthreading) and am in a situation where "as fast as possible" is
 important (while individual datasets may not grow, the number of them
 is).

 Any thoughts appreciated.

I would recommend you take a look at the new std.parallelism module, which was introduced in the most recent DMD release (2.053): http://www.d-programming-language.org/phobos-prerelease/ std_parallelism.html -Lars

http://www.codestrokes.com/archives/116 I hope it helps.
Jun 16 2011