similar to: clusterApply arguments

Thank you for your answer! I agree with you except for the 3 (Error) example and I realize now I should have started with that in the explanation. >From my point of view parLapply(cl = clu, X = 1:2, fun = fun, c = 1) shouldn't give an error. This could be easily avoided by using all the argument names in the custerApply call of parLapply which means changing, parLapply <-

On 03/15/2018 05:25 PM, Henrik Bengtsson wrote: > On Thu, Mar 15, 2018 at 3:39 AM, <FlorianSchwendinger at gmx.at> wrote: >> Thank you for your answer! >> I agree with you except for the 3 (Error) example and >> I realize now I should have started with that in the explanation. >> >> From my point of view >> parLapply(cl = clu, X = 1:2, fun = fun, c =

On Thu, Mar 15, 2018 at 3:39 AM, <FlorianSchwendinger at gmx.at> wrote: > Thank you for your answer! > I agree with you except for the 3 (Error) example and > I realize now I should have started with that in the explanation. > > From my point of view > parLapply(cl = clu, X = 1:2, fun = fun, c = 1) > shouldn't give an error. > > This could be easily avoided by

This is nothing specific to parallel::clusterApply() per se. It is the default behavior of R where it allows for partial argument names. I don't think there's much that can be done here except always using fully named arguments to the "apply" function itself as you show. You can "alert" yourself when there's a mistake by using: options(warnPartialMatchArgs =

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]:

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 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

Hello, is there any function in the package snow to check for a really running cluster? The function checkCluster only checks the variable cl. And the variable is still available after stopping the cluster! ( a simple solution would be deleting the cluster variable cl in the function stopCluster) > library(snow) > cl <- makeCluster(5) 5 slaves are spawned successfully. 0

Hi, Here are the issues I found: Typos ----- (a) Found: It a parallel version of ?evalq?, "is" missing. (b) Found: 'parLapplyLB', 'parSapplyLB' are load-balancing versions, intended for use when applying ?FUN? to 'parLapplyLB' has no 'FUN' arg (more on this below). (c) Found: 'clusterApply' calls 'fun' on the first

invoking parLapply without a cluster fails to find a previously registered cluster > library(parallel) > setDefaultCluster(makePSOCKcluster(2)) > parLapply(X=1:2, fun=function(...) {}) Error in cut.default(i, breaks) : invalid number of intervals This is because in parLapply length(cl) is determined before defaultCluster(cl) is called. By inspection, this appears to be true of

Hello everybody, I've got a bit of a problem with parLapply that's left me scratching my head today. I've tried this in R 2.11 and the 23 bit Revolution R Enterprise and gotten the same result, OS in question is Windows XP, the package involved is the snow package. I've got a list of 20 rain/no rain (1/0) situations for these two stations i and j, all the items in this list look

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

Hi, I want to compute a distribution of the intersection of a graph and 'randomized' graphs induced by the permutations of node labels (to preserve the graph topology). Since I ll have many permutations to perform, I was thinking of using the snow package and in particular "parSapply" to divide the work between my 4 CPUs. But I get the following error message : Error in

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

I'm using text miner (the "tm" package) to process large numbers of blog and message board postings (about 245,000). Does anyone have any advice for how to efficiently extract the meta data from a corpus of this size? TM does a great job of using MPI for many functions (e.g. tmMap) which greatly speed up the processing. However, the "meta" function that I need does not

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 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

Hi Simon, I'm having trouble with nested parallel workers, specifically, forking inside socket connections. When mclapply is called inside a SOCK, PSOCK or FORK worker I get an error in unserialize(). cl <- makeCluster(1, "SOCK") fun = function(i) { library(parallel) mclapply(1:2, sqrt) } Failure occurs after multiple calls to clusterApply: > clusterApply(cl, 1,

Dear All, I like to simulate a physiologically based pharmacokinetics model using R but am having a problem with the daspk routine. The same problem has been implemented in Berkeley madonna and Winbugs so that I know that it is working. However, with daspk it is not, and the numbers are everywhere! Please see the following and let me know if I am missing something... Thanks a lot in advance,

On 03/25/2015 07:48 PM, Simon Urbanek wrote: > On Mar 25, 2015, at 3:46 PM, Valerie Obenchain <vobencha at fredhutch.org> wrote: > >> Hi Simon, >> >> I'm having trouble with nested parallel workers, specifically, forking inside socket connections. >> > > You simply can't by definition - when you fork *all* the workers share the same connection