It's very system dependent right now. To parallelize fortran requires a compiler that supports it. gnu-fortran does not (outside of some experimental versions).<br><br>You need to decide early in the code how many threads you can launch and then divide your problem into that many chunks. Then it's spawn children with a set of number to crunch. With some slick params you can pin a process to a core so it won't get migrated around during system checks.<br>
<br>Or, as suggested, dump it to a gpu with cuda or opencl and let it handle the crunch.<br><br>Heh. I've done parallel bash.<br><br><div class="gmail_quote">On Fri, Mar 8, 2013 at 11:33 AM, Jeff Hubbs <span dir="ltr"><<a href="mailto:jhubbslist@att.net" target="_blank">jhubbslist@att.net</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">My *practical* experience has a hole in it when it comes to developing software to efficiently use multiple cores in a machine.<br>
<br>
If I'm writing code in the likes of C++, Python, or Fortran (acknowledging that I've got a range of programming paradigms there) and let's say that I'm subtracting two 2-D arrays of floating point numbers from one another element-wise, how is it that the operation gets blown across multiple CPU cores in an efficient way, if at all? Bear in mind that if this is done in Fortran, it's done in a pair of nested do-loops so unless the compiler is really smart, that becomes a serial operation.<br>
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