search for: embarrassingly_parallel

..., though inter-unit optimizations can take much longer, the benefits are worthwhile. Multiple threads/processes with a message passing interface in between them would be a start, but compiling a unix kernel that way would be tricky memory-wise. ;) cheers, --renato [1] http://en.wikipedia.org/wiki/Embarrassingly_parallel

On Saturday 21 November 2009 14:27:15 Chris Lattner wrote: > On Nov 19, 2009, at 1:04 PM, Bob Wilson wrote: > >> I've tested it and LLVM is indeed 2x slower to compile, although it > >> generates > >> code that is 2x faster to run... > >> > >>> Compared to a compiler in the same category as PCC, whose pinnacle of > >>> optimization

Hello, I'm working with a very large dataset in R on a computer running 64-bit Windows Server 2008 Standard with 32GB of RAM. According to the R for Windows FAQ, the maximum value allowed for max-mem-size is 4095MB. Is it possible to run R with a higher memory limit on this system? I've tried changing memory.limit() in the R console but it claims the system has a 4-GB address limit,

On Nov 19, 2009, at 1:04 PM, Bob Wilson wrote: >> I've tested it and LLVM is indeed 2x slower to compile, although it >> generates >> code that is 2x faster to run... >> >>> Compared to a compiler in the same category as PCC, whose pinnacle of >>> optimization is doing register allocation? I'm not surprised at all. >> >> What else

...amazed how quickly you can get up and > running. > > As suggested at the start of this email... "it depends"... > > Best Regards, > Sean O'Riordain > Dublin > > [1] http://cran.r-project.org/web/packages/biglm/index.html > [2] http://en.wikipedia.org/wiki/Embarrassingly_parallel > > > iwalters wrote: > > > > I'm currently working with very large datasets that consist out of > > 1,000,000 + rows. Is it at all possible to use R for datasets this size > > or should I rather consider C++/Java. > > > > > > > > -- &gt...