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Special Forums UNIX and Linux Applications High Performance Computing Programming parallel for C++ and Python application Post 302775393 by figaro on Monday 4th of March 2013 05:47:49 PM
Old 03-04-2013
Programming parallel for C++ and Python application

I am looking to have a C++ and Python application developed using the processing power of at least one GPU.
We are already using Boost, so Boost.MPI comes to mind.
We already have some experience in developing C++ applications for multi-core CPUs by using threading.
However, since we have not developed sufficient expertise in MPI, we were wondering if a Python solution would be more appropriate, because it will give the developers a better understanding of what the code does. In such a case something like mpi4py would come to mind.

Of course having a GPU specifically for simulation purposes could mean that something like PyOpenCL may be even better.
I mention "simulation", because the machine will be calculating variations on what is largely the same problem, ie not be a general purpose machine.

So in summary:
1- For raw processing speed with a multi-core CPU and a GPU, would a openCL solution or Boost.MPI solution be recommended?
2- Should the Python solution be used only for high level functionality, such as handling data upon return, or does Python compromise speed too much?
 

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IMPTRACE(1)						    BSD General Commands Manual 					       IMPTRACE(1)

NAME
imptrace -- report importance donation events in real time SYNOPSIS
imptrace [-i [-s]] [-p pid] [-d] DESCRIPTION
The imptrace utility displays a trace of importance donation events. Importance donation is used by adaptive jobs on the system to manage their priority on the system. See xpc_transaction_begin(3) and launchd.plist(5) for more information about the mechanism and its use. The options are as follows: -i Show internal kernel boosts -s Show stacks for internal boosts. -p pid Limit events to the process identified by pid. -d Display raw Dtrace output; do not reformat timestamps and sort output. The traced events are as follows: BOOSTED The specified process has received a boost and transitioned out of the background. UNBOOST The specified process has dropped its last remaining boost and transitioned back into the background. Recv Boost The specified process has received a boost and accepted ownership of that boost in userspace, usually by dequeuing the boost- ing message. Drop Boost The specified process has dropped a boost. ____ Int Boost Internal boost events are only emitted when tracking of kernel internal boosts is activated with the -i option. Their use and meaning is subject to change and dependent on the implementation details of importance donation. EXAMPLES
The imptrace script will output one line for each event, for example a typical boosting exchange might look as follows: 0000:00:00.000000000 EVENT PROCESS BOOSTS NOTES 0023:15:13.844332886 BOOSTED 22:configd 0023:15:13.844372519 Recv Boost 22:configd 1 from 275:SystemUIServer 0023:15:13.844497860 UNBOOST 22:configd Boosted for 0 ms 0023:15:13.844509452 Drop Boost 22:configd 0 In this case, SystemUIServer (PID 275) has sent a message to configd (PID 22) which caused it to be boosted. configd then dropped the boost causing it to be become unboosted and return to background state. Boosted and unboost events may appear before the triggering recv or drop boost. NOTES
imptrace is implemented using Dtrace. For information about the probes used, see comments in the imptrace source. When debugging an adap- tive service, it may be helpful to combine these probes with other Dtrace providers; however, they should be considered unstable. OS X
May 01, 2013 OS X
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