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Re: ParallelDo and C-compiled routines

  • To: mathgroup at smc.vnet.net
  • Subject: [mg121788] Re: ParallelDo and C-compiled routines
  • From: Gabriel Landi <gtlandi at gmail.com>
  • Date: Sun, 2 Oct 2011 02:36:36 -0400 (EDT)
  • Delivered-to: l-mathgroup@mail-archive0.wolfram.com
  • References: <j5ug1a$7r5$1@smc.vnet.net> <201109290605.CAA22485@smc.vnet.net> <201109300803.EAA06571@smc.vnet.net>

You can always try the 'old school' way. Use Mathematica as a command prompt:

ParallelDo[result[i] =ReadList[ "! ./c_code.exe", Number], {i,number}]

Works perfectly.




On Sep 30, 2011, at 5:03 AM, Patrick Scheibe wrote:

> On Thu, 2011-09-29 at 02:05 -0400, DmitryG wrote:
>> On Sep 28, 2:49 am, DmitryG <einsch... at gmail.com> wrote:
>>> Hi All,
>>>
>>> I am going to run several instances of a long calculation on =
different
>>> cores of my computer and then average the results. The program looks
>>> like this:
>>>
>>> SetSharedVariable[Res];
>>> ParallelDo[
>>> Res[[iKer]] = LongRoutine;
>>> , {iKer, 1, NKer}]
>>>
>>> LongRoutine is compiled. When compiled in C, it is two times faster
>>> than when compiled in Mathematica. In the case of a Do cycle, this
>>> speed difference can be seen, However, in the case of ParallelDo I
>>> have the speed of the Mathematica-compiled routine independently of
>>> the CompilationTarget in LongRoutine, even if I set NKer=1.
>>>
>>> What does it mean? Are routines compiled in C unable of parallel
>>> computing? Or there is a magic option to make them work? I tried
>>> Parallelization->True but there is no result, and it seems this =
option
>>> is for applying the routine to lists.
>>>
>>> Here is an example:
>>> ************************************************************
>>> NKer = 1;
>>>
>>> (* Subroutine compiled in Mathematica *)
>>> m = Compile[ {{x, _Real}, {n, _Integer}},
>>>        Module[ {sum, inc}, sum = 1.0; inc = 1.0;
>>>    Do[inc = inc*x/i; sum = sum + inc, {i, n}]; sum]];
>>>
>>> (* Subroutine compiled in C *)
>>> c = Compile[ {{x, _Real}, {n, _Integer}},
>>>        Module[ {sum, inc}, sum = 1.0; inc = 1.0;
>>>    Do[inc = inc*x/i; sum = sum + inc, {i, n}]; sum],
>>>   CompilationTarget -> "C"];
>>>
>>> (* There is a difference between Mathematica and C *)
>>> Do[
>>> Print[AbsoluteTiming[m[1.5, 10000000]][[1]]];
>>> Print[AbsoluteTiming[c[1.5, 10000000]][[1]]];
>>> , {iKer, 1, NKer}]
>>> Print[];
>>>
>>> (* With ParallelDo there is no difference *)
>>> ParallelDo[
>>> Print[AbsoluteTiming[m[1.5, 10000000]][[1]]];
>>> Print[AbsoluteTiming[c[1.5, 10000000]][[1]]];
>>> , {iKer, 1, NKer}]
>>> **************************************************************
>>>
>>> Any help?
>>>
>>> Best,
>>>
>>> Dmitry
>>
>> My theory is the following. C compiler creates an executable that is
>> saved somewhere on the hard drive and then run by Mathematica Kernel.
>> Windows may not allow different applications (such as different
>> Mathematica kernels in parallel computation) access a file at the =
same
>> time.
>>
>> If this is true, the solution were to create copies of this =
executable
>> on the hard drive, so that each kernel could run its copy.
>>
>> Dmitry
>>
>
> No, not exactly. The compiler creates a library which is a dll in your
> (Microsoft Windows) case or a shared object on Linux or a dylib on
> MacOSX.
>
> When you compile a function into "C" than a library is created and the
> library function of this dll|so|dylib is accessed when you call the
> compiled function in your Mathematica session.
>
> On my Linux box these created C-libraries are stored in my
> $UserBaseDirectory under
>
> $UserBaseDirectory/ApplicationData/CCompilerDriver/BuildFolder
>
> and then every unique MathKernel (with which you compile the function)
> gets its own subdirectory. This means, if my currently running
> MathKernel has an process id of, say 2088, I get a subdirectory
>
> warp-2088
>
> under the above mentioned folder. "warp" is here the name of my =
machine.
> This information is available in your "CompiledFunction" object too.
> Look at
>
> c // InputForm
>
> of your function and notice how Oleksandr show in his mail how to
> accesses this information to load the compiled function separately for
> each kernel.
>
> Beside the expanation of Oleksandr, which describes your behavior in
> detail, I just want to add, that you don't have to recompile a =
function
> everytime you restart the kernel. You could use LibraryGenerate to
> create a library which is permanently available (it seems that the
> libraries created with Compile[...,CompilationTarget->"C"] are deleted
> when the kernel quits). So with your MVM CompiledFunction you could
> create your lib with:
>
> << CCodeGenerator`
>
> m = Compile[{{x, _Real}, {n, _Integer}},
>   Module[{sum, inc}, sum = 1.0; inc = 1.0;
>    Do[inc = inc*x/i; sum = sum + inc, {i, n}]; sum]];
> LibraryGenerate[m, "longRoutine"]
>
>
> loadLib[] :=
>  LibraryFunctionLoad["longRoutine",
>   "longRoutine", {{Real, 0, "Constant"}, {Integer, 0, "Constant"}},
>   Real] ;
>
> brandNewC = loadLib[];
> NKer = 1;
> ParallelDo[
> brandNewC = loadLib[];
> Print[AbsoluteTiming[brandNewC[1.5, 10000000]]],
> {iKer, 1, NKer}
> ]
>
>
> Cheers
> Patrick
>
>




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