similar to: GPU Computing

Displaying 20 results from an estimated 3000 matches similar to: "GPU Computing"

2010 Nov 30
2
stats::kernel
Hi, There is a small bug in the kernel() function. Everything is fine when we use the format: kernel("name",m,r) but if we want the first argument to be a vector, which is useful is we are interested in using a method not implemented in kernel(), the default value of m is wrong. For example, if we do: s <- rep(1/11,6) k <- kernel(s) we get the error message Error in
2011 Nov 02
1
kernapply.ts
I have a suggestion for kernapply for ts objects. When we choose the option circular=F, the returned series don't have the correct dates. The removed dates are all at the beginning instead of half at the beginning and half at the end. It is particularly useful when we need to smooth the series (or remove a trend using a filter) before estimating a model (like in macroeconomics) or simply
2009 Dec 07
4
Announce: edtdbg, integrating R's debug() with your text editor
I've just developed edtdbg, a small package that integrates R's debug() with one's text editor. Excerpt from the README file: Goal The debug() function in R is primitive. My goal was to make it more usable by integrating it with one's text editor. Hence I wrote the package here, edtdbg. Its features include: * As one steps from line to line of code
2011 Oct 19
3
new R debugging tool
I've developed a new R debugging tool, debugR, available at http://heather.cs.ucdavis.edu/debugR.html This basically replaces my edtdbg, which I will no longer be supporting. The new tool is now decoupled from one's text editor, and has a lot more features than edtdbg did. Try it! Feedback is encouraged, of course. Norm Matloff
2012 Dec 11
1
Bug in mclapply?
I've been using mclapply and have encountered situations where it gives errors or returns incorrect results. Here's a minimal example, which gives the error on R 2.15.2 on Mac and Linux: library(parallel) f <- function(x) NULL mclapply(1, f, mc.preschedule = FALSE, mc.cores = 1) # Error in sum(sapply(res, inherits, "try-error")) : # invalid 'type' (list) of argument
2013 Feb 02
1
best practice for packages using mclapply to avoid tcltk
Dear R-devel friends: I'm back to bother you again about the conflict between mclapply and tcltk. I've been monitoring several packages that want to use mclapply to parallelize computations and need to figure out what should be done. It appears tcltk cannot be safely unloaded, so the best we can do is check for the presence of tcltk and stop if it is found before mclapply() is used. I
2015 Jul 24
1
Memory limitations for parallel::mclapply
Hello, I have been having issues using parallel::mclapply in a memory-efficient way and would like some guidance. I am using a 40 core machine with 96 GB of RAM. I've tried to run mclapply with 20, 30, and 40 mc.cores and it has practically brought the machine to a standstill each time to the point where I do a hard reset. When running mclapply with 10 mc.cores, I can see that each process
2011 Feb 01
1
using SNOW and clusterApplyLB to run jobs parallel
I have this function and want to run it parallel with different sets of data. Using SNOW and clusterApplyLB. system.time(out <- mclapply(cData, plotGraph)) #each cData contains 100X6000 doubles system.time(out <- mclapply(cData2, plotGraph)) system.time(out <- mclapply(cData3, plotGraph)) system.time(out <- mclapply(cData4, plotGraph)) system.time(out <- mclapply(cData5,
2015 Feb 26
1
iterated lapply
> On Feb 25, 2015, at 5:35 PM, Benjamin Tyner <btyner at gmail.com> wrote: > > Actually, it depends on the number of cores: Under current semantics, yes. Each 'stream' of function calls is lazily capturing the last value of `i` on that core. Under Luke's proposed semantics (IIUC), the result would be the same (2,4,6,8) for both parallel and serial execution. This is
2023 May 16
1
mclapply enters into an infinite loop....
Dear members, I am using arfima in an mclapply construction (from the parallel package): Browse[2]> LYG <- mclapply(LYGH, FUN = arfima, mc.cores = detectCores()) ^C Browse[2]> LYG <- mclapply(LYGH[1:10], FUN = arfima, mc.cores = detectCores()) ^C Browse[2]> LYG <- mclapply(LYGH[1:2], FUN = arfima, mc.cores = detectCores()) ^C You can see that I am
2011 Mar 22
2
Problem with mclapply -- losing output/data
Hello, I am running large simulations, which unfortunately I can't really replicate here because the code is so extensive. I rely heavily on mclapply, but I realize that I'm losing data somewhere. There are two worrisome symptoms: 1) I am getting 'NULL' as a return value for some (but not all) elements of the output when I use mclapply, but not if I use lapply > tmp2[1:3]
2012 Dec 13
1
possible bug in function 'mclapply' of package parallel
Dear parallel users and developers, I might have encountered a bug in the function 'mclapply' of package 'parallel'. I construct a matrix using the same input data and code with a single difference: Once I use mclapply and the other time lapply. Shockingly the result is NOT the same. To evaluate please unpack the attached archive and execute Rscript mclapply_test.R I put the
2023 May 17
1
mclapply enters into an infinite loop....
Dear Jeff, There was a problem in LYGH and lapply threw an error, but mclapply got stuck in an infinite loop. The doc for mclapply says that mclapply runs under try() with silent = TRUE. So that means mclapply should run properly, i.e output a try class object and exit. But it didn't. Can you shed some light on why this happened? THanking you, Yours sincerely, AKSHAY M
2019 Apr 13
3
SUGGESTION: Settings to disable forked processing in R, e.g. parallel::mclapply()
Hi Inaki, > "Performant"... in terms of what. If the cost of copying the data > predominates over the computation time, maybe you didn't need > parallelization in the first place. Performant in terms of speed. There's no copying in that example using `mclapply` and so it is significantly faster than other alternatives. It is a very simple and contrived example, but
2018 Mar 04
0
Change Function based on ifelse() condtion
The reason that it works for Apply_MC=TRUE is that in that case you call mclapply(X,FUN,...) and the mclapply() function strips off the mc.cores argument from the "..." list before calling FUN, so FUN is being called with zero arguments, exactly as it is declared. A quick workaround is to change the line Lapply_me(as.list(1:4), function(xx) { to Lapply_me(as.list(1:4),
2020 Apr 28
2
mclapply returns NULLs on MacOS when running GAM
Thanks Henrik, That clears things up significantly. I did see the warning but failed to include it my initial email. It sounds like an RStudio issue, and it seems like that it?s quite intrinsic to how forks interact with RStudio. Given this code is eventually going to be a part of a package, should I expect it to fail mysteriously in RStudio for my users? Is the best solution here to migrate all
2019 Apr 05
2
Deep Replicable Bug With AMD Threadripper MultiCore
The following program is whittled down from a much larger program that always works on Intel, and always works on AMD's threadripper with lapply but not mclappy. With mclapply on AMD, all processes go into "suspend" mode and the program then hangs. This bug is replicable on an AMD Ryzen Threadripper 2950X 16-Core Processor (128GB RAM), running latest ubuntu 18.04. The R version
2015 Feb 24
2
iterated lapply
> On Feb 24, 2015, at 10:50 AM, <luke-tierney at uiowa.edu> wrote: > > The documentation is not specific enough on the indented semantics in > this situation to consider this a bug. The original R-level > implementation of lapply was > > lapply <- function(X, FUN, ...) { > FUN <- match.fun(FUN) > if (!is.list(X)) > X <-
2020 Apr 29
2
mclapply returns NULLs on MacOS when running GAM
Thanks Simon, I will take note of the sensible default for core usage. I?m trying to achieve small scale parallelism, where tasks take 1-5 seconds and make fuller use of consumer hardware. Its not a HPC-worthy computation but even laptops these days come with 4 cores and I don?t see a reason to not make use of it. The goal for the current piece of code I?m working on is to bootstrap many
2018 Mar 04
2
Change Function based on ifelse() condtion
Hi, As an example, I want to create below kind of custom Function which either be mclapply pr lapply Lapply_me = function(X = X, FUN = FUN, ..., Apply_MC = FALSE) { if (Apply_MC) { return(mclapply(X, FUN, ...)) } else { if (any(names(list(...)) == 'mc.cores')) { list(...) = list(...)[!names(list(...)) %in% 'mc.cores'] } return(lapply(X, FUN, ...)) } } However when Apply_MC =