search for: 5e8

...Even if replaced with something using the same interface (e.g., a function that automatically detects variables to export as in the amazing `future` package), the lack of copy-on-write functionality would cause scripts everywhere to break. A simple example illustrating these two points: `x <- 5e8; mclapply(1:24, sum, x, 8)` Using fork, `mclapply` takes 5 seconds. Using "psock", `clusterApply` does not complete. Travers On Fri, Apr 12, 2019 at 2:32 AM I?aki Ucar <iucar at fedoraproject.org> wrote: > > On Thu, 11 Apr 2019 at 22:07, Henrik Bengtsson > <henrik.be...

...cripts to break unexpectedly: everything works fine, > until you change a small unimportant bit and... boom, out of memory. > And in general, running forks in any GUI would cause things everywhere > to break. > > > A simple example illustrating these two points: > > `x <- 5e8; mclapply(1:24, sum, x, 8)` > > > > Using fork, `mclapply` takes 5 seconds. Using "psock", `clusterApply` > > does not complete. > > I'm not sure how did you setup that, but it does complete. Or do you > mean that you ran out of memory? Then try replacing &...

...ry, and this is what causes scripts to break unexpectedly: everything works fine, until you change a small unimportant bit and... boom, out of memory. And in general, running forks in any GUI would cause things everywhere to break. > A simple example illustrating these two points: > `x <- 5e8; mclapply(1:24, sum, x, 8)` > > Using fork, `mclapply` takes 5 seconds. Using "psock", `clusterApply` > does not complete. I'm not sure how did you setup that, but it does complete. Or do you mean that you ran out of memory? Then try replacing "x" with, e.g., &quo...

ISSUE: Using *forks* for parallel processing in R is not always safe. The `parallel::mclapply()` function uses forked processes to parallelize. One example where it has been confirmed that forked processing causes problems is when running R via RStudio. It is recommended to use PSOCK clusters (`parallel::makeCluster()`) rather than *forked* processes when running R from RStudio (

Dear All, I'm wondering if there is a R function could give me all the combinations of the grouping/cluster result, given the number of the groups. e.g. 3 objects: x1 x2 x3, number of groups is 2 so the result will be group1:x1,x2; group2: x3 group1: x1;group2: x2,x3 group1: x1,x3;group2: x2 many thanks yan

...everything works fine, > > until you change a small unimportant bit and... boom, out of memory. > > And in general, running forks in any GUI would cause things everywhere > > to break. > > > > > A simple example illustrating these two points: > > > `x <- 5e8; mclapply(1:24, sum, x, 8)` > > > > > > Using fork, `mclapply` takes 5 seconds. Using "psock", `clusterApply` > > > does not complete. > > > > I'm not sure how did you setup that, but it does complete. Or do you > > mean that you ran out o...

Dear list, I have noticed surprisingly big performance differences of runif() between Windows XP and (Debian) linux on similar CPUs (Pentium D 3.0GHz (WinXP)/3.2GHz (Linux)) and I wonder if there is a simple explanation for the difference. On a linux system (with a slightly better CPU and 1GB more RAM), execution of runif() seems to consume about 80% more CPU time than on a Windows XP

...> +000005e4 <__FUNCTION__.22896>: > 5e4: 6d insl (%dx),%es:(%edi) > 5e5: 61 popa > - 5e6: 72 6b jb 653 <__FUNCTION__.20708+0x4> > + 5e6: 72 6b jb 653 <__FUNCTION__.20622+0x4> > 5e8: 5f pop %edi > 5e9: 63 6f 6e arpl %bp,0x6e(%edi) > - 5ec: 73 74 jae 662 <__FUNCTION__.20708+0x13> > + 5ec: 73 74 jae 662 <__FUNCTION__.20622+0x13> > 5ee: 61 popa > 5ef: 6e...

The saga continues. I've been tracking the interface changes and merging them with the refactoring work I'm doing. I got as far as building stage3 of llvm-gcc but the object files from stage2 and stage3 differ: warning: ./cc1-checksum.o differs warning: ./cc1plus-checksum.o differs (Are the above two ok?) The list below is clearly bad. I think it's every object file in the