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 Maybe sometimes the "old school" is for some purposes just outdated.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 >>> >>> >> >> > >

**References**:**Re: ParallelDo and C-compiled routines***From:*Gabriel Landi <gtlandi@gmail.com>

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