similar to: parallel::detectCores() bug on Raspberry Pi B+

Hi, Both under the current R-devel (r66456) and a version from about 3 months ago, I experience the following behavior: > parallel::detectCores(TRUE) Error in system(cmd, TRUE) : error in running command > traceback() 3: system(cmd, TRUE) 2: gsub("^ +", "", system(cmd, TRUE)[1]) 1: parallel::detectCores(TRUE) > This is on Ubuntu 14.04. Does anybody else see this? [I

Hi all. I’ve been working with Swift on ARMv6 and v7. While working with ARMv6 on linux, I noticed that my arm architecture canonicalization code didn’t produce the expected result. The code that I had been using (within Swift’s Driver.cpp the following: static llvm::Triple computeTargetTriple(StringRef DefaultTargetTriple) { llvm::Triple triple = llvm::Triple(DefaultTargetTriple); //

Dear Tomas, thank you very much. I installed r-devel r75201 and tested. The machine with 88 cores has NUMA disabled. It therefore has 2 processor groups with 64 and 24 processors each. require(parallel) detectCores() # [1] 88 This is great! Then I went on to test with a simple 'foreach()' loop. I started with 64 processors (max limit of 1 processor group). I ran with a simple function

Dear Tomas, thank you for looking into this. Here's the output: # number of logical processors - what detectCores() should return out <- system("wmic cpu get numberoflogicalprocessors", intern=TRUE) [1] "NumberOfLogicalProcessors \r" "22 \r" "22 \r" [4] "20 \r"

Dear R-devel list, R's detectCores() function internally calls "ncpus" function to get the total number of logical processors. However, this doesnot seem to take NUMA into account on Windows machines. On a machine having 48 processors (24 cores) in total and windows server 2012 installed, if NUMA is enabled and has 2 nodes (node 0 and node 1 each having 24 CPUs), then R's

I saw in the anouncement for the upcoming Boston, MA conference that there will be demos of CentOS on an ARM7 system. I am wondering: does this mean that there is somewhere out there a build of some version of CentOS that might run on a Raspberry Pi (which has an ARM7 processor)? -- Robert Heller -- 978-544-6933 Deepwoods Software -- Custom Software Services

Thanks for the clarifications, Bob! I’ve spent some time with the head of the llvm.org repo, and I now understand a lot better what Renato and Tim were talking about re. the architecture aliases. The patch to add v6l, therefore, seems simple enough. I haven’t been able to test it in my usual flow, because that involves the whole swift stack. I’m considering creating a program that links to

In the 'parallel' package there is detectCores(), which tries its best to infer the number of cores on the current machine. This is useful if you wish to utilize the *maximum* number of cores on the machine. Several are using this to set the number of cores when parallelizing, sometimes also hardcoded within 3rd-party scripts/package code, but there are several settings where you wish to

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

Hi, IMO we should support this, even though if given the option I'd have asked the linux guys not to invent a new triple. It's in linux now, and `uname -a` is a very standard way of obtaining the host's triple. James On Tue, 5 Jan 2016 at 08:34 Tim Northover via llvm-dev < llvm-dev at lists.llvm.org> wrote: > On 4 January 2016 at 14:42, Artyom Skrobov via llvm-dev >

Taking the suggestions of the group under consideration, I’ve generated a new diff. The thing to note is that armv6l is now treated identically to armv6hl. I’ve also added a unit test. This seems to me to be the least invasive method, and holds to existing conventions as closely as possible. Thoughts? -------------- next part -------------- A non-text attachment was scrubbed... Name:

Dear expeRts, I struggle with the following problem using snow clusters for parallel computing: I would like to specify l'Ecuyer's random number generator. Base R creates a .Random.seed of length 7, the first value indicating the kind fo random number generator. I would thus like to use the components 2 to 7 as the seed for l'Ecuyer's random number generator. By doing so, I

Like so many others, I'm having NT_STATUS_LOGON_FAILURE issues. I've tried all the fixes I could find to no avail. My environment: Cent 7 (Linux 4.19.72-v7l.1.el7) with Samba 4.9.1, bound to AD via Realmd. SSSD for ACL's, winbind for user map. Installed packages: nano, ntpdate, ntp, realmd, sssd, sssd-tools, sssd-winbind-idmap, samba-winbind, adcli, oddjob, oddjob-mkhomedir,

Oops, I neglected to reply-all…. The current stable branch at github still has it: https://github.com/apple/swift-llvm/blob/stable/include/llvm/Support/ARMTargetParser.def#L106 <https://github.com/apple/swift-llvm/blob/stable/include/llvm/Support/ARMTargetParser.def#L106> Should I get the head of the non-swift repository and generate a new diff? Also, I suspect that it’s not a good idea

Hi all, I can't get the functionality of the package parallel to work. Specifically, makeCluster() hangs when I run it. I first noticed the problem when trying to run Rstan with multiple cores and the traced it back to the core package parallel. The following results in R hanging after the call to makeCluster. library(parallel) # Calculate the number of cores no_cores <-

On Tue, Jan 8, 2013 at 3:04 PM, Renato Golin <renato.golin at linaro.org> wrote: > The following failures are consistent on buildbot (and my local box). [...] > LLVM :: Transforms/LoopStrengthReduce/post-inc-icmpzero.ll > LLVM :: Transforms/LoopStrengthReduce/2012-07-18-LimitReassociate.ll It is interesting that I don't see this on my ARM box. Instead I see these: Failing

I've started to use the parallel package and it works very well speeding things up. Thank you for making this easy to do. Should I have expected that parCapply would return a vector when parApply returns a matrix? library(parallel) x <- matrix(rnorm(8), nc = 2) apply(x, 2, function(y) y) [,1] [,2] [1,] -0.9649685 0.91339851 [2,] -1.4313140 0.13457671 [3,] 1.0499248

after the last few days playing around with 4.1.17 I decided to start new and try 4.2 --- Hardware, OS: Pi B+, Raspbian 2015-02-16 --- Getting packages: - install packages: build-essential libacl1-dev libattr1-dev libblkid-dev libgnutls-dev libreadline-dev python-dev libpam0g-dev python-dnspython gdb pkg-config libpopt-dev libldap2-dev dnsutils libbsd-dev attr krb5-user docbook-xsl

Hi R-developers In the package Parallel, the function parLapply(cl, x, f) seems to allow transmission of only one parameter (x) to the function f. Hence in order to compute f(x, y) parallelly, I had to define f(x, y) as f(x) and tried to access y within the function, whereas y was defined outside of f(x). Script: library(parallel) f <- function(x) { z <- 2 * x + .GlobalEnv$y # Try to

>> However, because the DefaultTargetTriple is armv6l-unknown-linux-gnueabihf, >> and llvm didn’t know about v6l, it would fail to match and canonicalize to armv6. >> I added the notion of v6l to llvm to address this. > > ARMv6l was definitely there once. I'm not sure what happened. > > I'm copying the ARM folks that did most of the recent changes in hope