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Re: ParallelDo and C-compiled routines
*To*: mathgroup at smc.vnet.net
*Subject*: [mg121816] Re: ParallelDo and C-compiled routines
*From*: Gabriel Landi <gtlandi at gmail.com>
*Date*: Mon, 3 Oct 2011 04:24:07 -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> <201110020636.CAA28021@smc.vnet.net> <1317548615.1722.15.camel@warp>
Yes, you are absolutely right Patrick.
The only thing I disagree is the "as easily available".
I have a code that solves stochastic differential equations and I have
tried to implement it in Mathematica.
So far I have had a pretty hard time. Can you point me in the right
direction? It is written in C++.
Is there an easy way to add the wolfram libraries so that it can be
accessed inside Mathematica?
Thanks in advance,
Gabriel
On Oct 2, 2011, at 6:43 AM, Patrick Scheibe wrote:
> The communication overhead is beyond good and evil. I assumed someone
> who asked a question about ParallelDo is kind of concerned about
> *speed*:
>
> In[19]:= First@
> AbsoluteTiming@
> Table[ReadList["! tmp/square.exe " <> ToString[i], Number], {i,
> 10000}]
>
> Out[19]= 48.957941
>
> the equivalent library function:
>
> In[18]:= First@AbsoluteTiming@Table[cfun, {i, 10000}]
>
> Out[18]= 0.005934
>
> I admit, that a function like "square" is a bit too short for a
> "longRoutine", but why using this kind of communication when the faster
> solution is as easily available?
> Maybe sometimes the "old school" is for some purposes just outdated.
>
> Cheers
> Patrick
>
> On Sun, 2011-10-02 at 02:36 -0400, Gabriel Landi wrote:
>> 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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