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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