multicore 0.1-4 for Windows
On Sat, 2009-08-15 at 22:02 +0800, huang min wrote:
Yep. I got similar results. Some person recommands to use some non-trival examples. My concern is that the package doesn't give the correct answer even for the non-trival example, how can you expect it to work for other examples? See one of my example:
I didn't make the suggestion, but in the recent case the advice to use non-trivial examples concerned performance measurement. For correctness, the simplest failure is the best. Ross
mclapply(1:10, function(x)rnorm(x), mc.cores=1)
[[1]] [1] -0.3462702 [[2]] [1] -2.02105262 -0.09585892 [[3]] [1] 1.77221975 -0.46601980 -0.07315387 [[4]] [1] -2.1283152 -0.6079173 0.4305617 0.3908323 [[5]] [1] -1.1565899 2.5569418 2.1897070 1.0436908 -0.5969151 [[6]] [1] 0.9828604 -0.1479499 -0.1284607 -0.3347436 1.8018194 -1.4417220 [[7]] [1] 0.55425627 -0.09030843 -0.60091740 0.12777185 -0.13998607 0.35154851 [7] -0.04631663 [[8]] [1] -0.35164946 0.97417980 -1.49269589 -0.18409462 0.06105646 0.26404434 [7] -0.18976067 -1.14565065 [[9]] [1] 0.22496664 1.06111320 2.01179778 -0.27569373 -0.42400996 -0.05679749 [7] 0.30982861 1.52450703 -0.21109130 [[10]] [1] -1.3539043 -1.3596462 0.5039675 -1.1326639 0.2802417 -0.1049306 [7] -0.3243349 0.4130826 0.9384500 1.3505106
mclapply(1:10, function(x)rnorm(x), mc.cores=2)
[[1]] [1] -0.6564236 [[2]] [1] 0.2981595 -0.9662663 -0.1586778 [[3]] [1] 0.20283018 1.91891067 -0.03065513 -1.25895660 -1.43237050 [[4]] [1] -0.97452271 1.95556392 -0.01407120 -1.45767782 0.83977490 1.20149231 [7] -0.65395606 [[5]] [1] -0.36855293 0.57518030 -1.52608826 -1.01052287 0.07453356 -1.47265355 [7] -0.05276850 -0.97598956 -0.43125376
mclapply(1:10, function(x)rnorm(x), mc.cores=2)
list()
mclapply(1:10, function(x)rnorm(x), mc.cores=2)
list()
mclapply(1:10, function(x)rnorm(x), mc.cores=2)
[[1]] NULL [[2]] [1] 0.4338477 -0.2756795 [[3]] NULL [[4]] [1] -0.08792539 -0.21166520 -0.07175587 -1.44629997 [[5]] NULL [[6]] [1] -0.11538155 1.47215456 0.12418766 -0.09469826 1.24639136 -2.08412950 [[7]] NULL [[8]] [1] 0.59884313 -0.69780323 -1.50954506 -0.04156023 0.20434899 -0.59812782 [7] 0.88432575 -0.41230545 [[9]] NULL [[10]] [1] -2.2547262 -0.2821773 0.8624573 0.4983850 0.5977264 1.8459354 [7] -0.8028298 1.0817651 -2.0418556 -0.2725846
It even fails to generate the correct number of random numbers. How can I use this package to do simulations? On Tue, Aug 11, 2009 at 10:18 PM, <davidr at rhotrading.com> wrote:
I understand that the support for Windows is experimental, so I thought I'd just try it out. Maybe I don't understand some things. Can someone explain what's going on here? Is this as expected, and what am I doing wrong? (I am on Windows XP SP2, and have 8 cores. SessionInfo below.)
install.packages('multicore',,'http://www.rforge.net/')
trying URL 'http://www.rforge.net/bin/windows/contrib/2.9/multicore_0.1-4.zip' Content type 'application/zip' length 90538 bytes (88 Kb) opened URL downloaded 88 Kb package 'multicore' successfully unpacked and MD5 sums checked The downloaded packages are in C:\Documents and Settings\davidr\Local Settings\Temp\Rtmpv52Ozb\downloaded_packages updating HTML package descriptions
library(multicore) mclapply(1:10, sqrt, mc.cores=1)
[[1]] [1] 1 [[2]] [1] 1.414214 [[3]] [1] 1.732051 [[4]] [1] 2 [[5]] [1] 2.236068 [[6]] [1] 2.449490 [[7]] [1] 2.645751 [[8]] [1] 2.828427 [[9]] [1] 3 [[10]] [1] 3.162278
mclapply(1:10, sqrt, mc.cores=2)
[[1]] [1] 1 [[2]] [1] 1.732051 [[3]] [1] 2.236068 [[4]] [1] 2.645751 [[5]] [1] 3
sessionInfo()
R version 2.9.1 (2009-06-26)
i386-pc-mingw32
locale:
LC_COLLATE=English_United States.1252;LC_CTYPE=English_United
States.1252;LC_MONETARY=English_United
States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] multicore_0.1-4
loaded via a namespace (and not attached):
[1] tools_2.9.1
Thanks for any insight,
David L. Reiner
Rho Trading Securities, LLC
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