search for: setmklthread

It runs more than twice as slowly using 8 core than using a single core in inverting large matrix. Tested on 8 core Windows XP 64 machine. > n = 1000 > n.simu = 100 > func1 = function() + { + x = rnorm(n*n) + dim(x)=c(n,n) + y = solve(x) + } > > setMKLthreads(1) > system.time(for(i in 1:n.simu) func1()) user system elapsed 69.48 2.42 71.91 > > setMKLthreads(8) > system.time(for(i in 1:n.simu) func1()) user system elapsed 179.06 17.90 41.70 Jason Liao [[alternative HTML version deleted]]

...st\intel64\compiler to C:\Program Files\R\R-3.6.1\bin\x64 Inside the destination folder, create 2 copies of mkl_rt.dll and rename one of them Rblas.dll and the other Rlapack.dll replacing the originals and also keeping mkl_rt.dll. 3. Slight problem here is that we are still lacking the setMKLthreads() and getMKLthreads() functions to set the nr of intel MKL threads, unless there is a CRAN package to do this? Would any of you be aware of this? In MRO these are in the RevoUtilsMath package, so one could still have to copy this across from MRO, or else just stick with the default nr of threads e...

Hi everyone, Thanks for the help in advance. I just want to know if there is absolutely any way of using multicore if my loop can't run in parallel as each iteration depends on the previous iteration. I have Windows R 2.9.2 and REvolution R Enterprise 2.0 for Windows 64 bit. I also have if necessary R on Ubuntu. 8gb of ram and a core 2 duo processor but I could use a quad core as well. Many