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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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WAPITI(1)							   User Commands							 WAPITI(1)

NAME
wapiti - a web application vulnerability scanner. SYNOPSIS
wapiti http://server.com/base/url/ [options] DESCRIPTION
Wapiti allows you to audit the security of your web applications. It performs "black-box" scans, i.e. it does not study the source code of the application but will scans the webpages of the deployed webapp, looking for scripts and forms where it can inject data. Once it gets this list, Wapiti acts like a fuzzer, injecting payloads to see if a script is vulnerable. OPTIONS
-s, --start <url> specify an url to start with. -x, --exclude <url> exclude an url from the scan (for example logout scripts) you can also use a wildcard (*): Example : -x "http://server/base/?page=*&module=test" or -x "http://server/base/admin/*" to exclude a directory -p, --proxy <url_proxy> specify a proxy (-p http://proxy:port/) -c, --cookie <cookie_file> use a cookie -t, --timeout <timeout> set the timeout (in seconds) -a, --auth <login%password> set credentials (for HTTP authentication) doesn't work with Python 2.4 -r, --remove <parameter_name> removes a parameter from URLs -m, --module <module> use a predefined set of scan/attack options: GET_ALL: only use GET request (no POST) GET_XSS: only XSS attacks with HTTP GET method POST_XSS: only XSS attacks with HTTP POST method -u, --underline use color to highlight vulnerable parameters in output -v, --verbose <level> set the verbosity level: 0: quiet (default), 1: print each url, 2: print every attack -h, --help print help page EFFICIENCY
Wapiti is developed in Python and use a library called lswww. This web spider library does the most of the work. Unfortunately, the html parsers module within python only works with well formed html pages so lswww fails to extract information from bad-coded webpages. Tidy can clean these webpages on the fly for us so lswww will give pretty good results. In order to make Wapiti far more efficient, you should: apt-get install python-utidylib python-ctypes AUTHOR
Copyright (C) 2006-2007 Nicolas Surribas <nicolas.surribas@gmail.com> Manpage created by Thomas Blasing <thomasbl@pool.math.tu-berlin.de> http://wapiti.sourceforge.net/ July 2007 WAPITI(1)
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