similar to: [parallel-package] feature request: set default cluster type via environment variable

Dear Tomas, Thanks for your commitment to fix this issue and also to add the chunk size as an argument. If you want our input, let us know ;) Best Regards On 02/26/2018 04:01 PM, Tomas Kalibera wrote: > Dear Christian and Henrik, > > thank you for spotting the problem and suggestions for a fix. We'll probably add a chunk.size argument to parLapplyLB and parLapply to follow OpenMP

Dear R-Devel List, I have installed R 3.4.3 with the patch applied on our cluster and ran a *real-world* job of one of our users to confirm that the patch works to my satisfaction. Here are the results. The original was a series of jobs, all essentially doing the same stuff using bootstrapped data, so for the original there is more data and I show the arithmetic mean with standard deviation. The

Dear Henrik, The rationale is just that it is within these extremes and that it is really simple to calculate, without making any assumptions and knowing that it won't be perfect. The extremes A and B you are mentioning are special cases based on assumptions. Case A is based on the assumption that the function has a long runtime or varying runtime, then you are likely to get the best load

Dear R-Devel List, **TL;DR:** The function **parLapplyLB** of the parallel package has [reportedly][1] (see also attached RRD output) not been doing its job, i.e. not actually balancing the load. My colleague Dirk Sarpe and I found the cause of the problem and we also have a patch to fix it (attached). A similar fix has also been provided [here][2]. [1]:

Dear Christian and Henrik, thank you for spotting the problem and suggestions for a fix. We'll probably add a chunk.size argument to parLapplyLB and parLapply to follow OpenMP terminology, which has already been an inspiration for the present code (parLapply already implements static scheduling via internal function staticClusterApply, yet with a fixed chunk size; parLapplyLB already

Hi, I'm trying to understand the rationale for your proposed amount of splitting and more precisely why that one is THE one. If I put labels on your example numbers in one of your previous post: nbrOfElements <- 97 nbrOfWorkers <- 5 With these, there are two extremes in how you can split up the processing in chunks such that all workers are utilized: (A) Each worker, called

Dear R developers, with the inclusion of the package "parallel" in the upcoming release of R, users and package developers are likely to make increasing usage of parallelization features. In part, these features rely on forking the R process. As ?mcfork points out, fork()ing in a GUI process is typically a bad idea. In RKWard, we "only" seem to have problems with signals

On 25/08/2017 1:19 PM, Sam Albers wrote: > Hi there, > > I am trying to distinguish between getOption() and Sys.getenv(). My > understanding is that these are both used to set values for variables. > getOption is set something like this: option("var" = "A"). This can be > placed in an .Rprofile or at the top of script. They are called like this >

Hi there, I am trying to distinguish between getOption() and Sys.getenv(). My understanding is that these are both used to set values for variables. getOption is set something like this: option("var" = "A"). This can be placed in an .Rprofile or at the top of script. They are called like this getOption("var"). Environmental variables are set in the .Renviron file

Heja, It took me a while to find a way of implementing normal text as subscripted text in a expression followed by another expression and more subscripted text :O) It looks now as this: ylab=expression(Delta^"+"~""[paste("my subscripted text")]~">"~Delta^"+"~""[paste("my subscripted text")]) I use several par commands

Hello, I am using function makePSOCKcluster to make parallel computation on 3 EC2 Amazon machines. I have a passwordless between machines and ssh is correct. In the R 2.15.1 release this function works correctly. Installing R 3.0.1 on my EC2 machines makePSOCKcluster does not produce the cluster. If I run the function with outfile="" option, I obtain this message Error in

For the simple C program below I show the output of clang and the output of the VS compiler (I am on windows). Maybe this is obvious to you, but is it really faster to do 2 multiplications, 3 movl instructions, 2 shifts, 1 add, and 1 substract than to do 1 mov, 1 cdq, and 1 idiv? I run into this while trying to understand why my code runs slower with llvm than a comparable program on windows.

Dear All I have sucessfully created cluster of four nodes using localhost in my local machine by executing the following command > cl<-makePSOCKcluster(c(rep("localhost",4)),outfile='',homogeneous=FALSE,port=11001) starting worker pid=4271 on localhost:11001 at 12:12:26.164 starting worker pid=4280 on localhost:11001 at 12:12:26.309 starting worker pid=4289 on

Hi all, Is it possible to use the 'renice' option together with parallel clusters of type 'PSOCK'? The help page for parallel::makeCluster is not specific about which options are supported on which types and I am getting the following message when passing renice = 19 : > cl <- parallel::makeCluster(2, renice = 19) nice: ?+19?: No such file or directory Kind regards,

dear all, I want to perform a posthoc test for my ANCOVA: a1<-aov(seeds~treatment*length) With summary(glht(a1, linfct = mcp(treatment = "Tukey"))) R tells me: "covariate interactions found -- please choose appropriate contrast" How do I build these contrasts? Ideally, I would like to have the posthoc test for the ANCOVA including a block-effect

Hi, -Does anyone know where a backend-specific optimization can be added to replace an instruction with code containing control flow? I'm interested in adding an optimization for the DIV instruction (x86-atom) which replace the IDIV/DIV with code containing control flow to select between the intended IDIV/DIV and an 8-bit DIV with movzx, as described in the Intel Atom Optimization Guide. My

Looks like a bug to me due to wrong assumptions about 'nice' arguments, but could be because a "non-standard" 'nice' is used. If we do: > trace(system, tracer = quote(print(command))) Tracing function "system" in package "base" we see that the system call used is: > cl <- parallel::makePSOCKcluster(2L, renice = 19) Tracing system(cmd, wait

Hi Henrik, Thanks for the detailed in fast reply! My guess would be that the confusion comes from the different use of nice and renice. The workraund you provided work fine! Thanks a lot. Best, Andreas Henrik Bengtsson <henrik.bengtsson at gmail.com> writes: > Looks like a bug to me due to wrong assumptions about 'nice' > arguments, but could be because a

http://llvm.org/docs/CodeGenerator.html#machine-code-description-classes Section starting: Fixed (preassigned) registers It talks about converting: define i32 @test(i32 %X, i32 %Y) { %Z = udiv i32 %X, %Y ret i32 %Z } into ;; X is in EAX, Y is in ECX mov %EAX, %EDX sar %EDX, 31 idiv %ECX ret BUT, where does the "sar" come from? Kind Regards James

(Bcc: CRAN) This is a proposal helping CRAN and alike as well as individual developers to avoid stray R processes being left behind that might be produced when an example or a package test fails to set up a parallel::makeCluster(). ISSUE If a package test sets up a PSOCK cluster and then the master process dies for one reason or the other, the PSOCK worker processes will remain running for 30