similar to: SVM Param Tuning with using SNOW package

Greetings, I am having an interesting problem and I wonder if anyone else has seen this behavior. I am running R 2.11.1 with SNOW 0.3-3 on a Dell cluster running CentOS 5.5. I create my cluster using: cluster<- makeCluster(nodes,type="SOCK",port=10191) # nodes is a vector of compute nodes I then wrap a loop around clusterApplyLB to evaluate my function multiple times, with

Hi, I am currently building an R package and I am facing a peculiar problem where some of the functions does not work within the package. However, if I source the script the function works. For example, in a method for parallelization of analysis on each chromosome simultaneously I am receiving error at the following position of the code: # this profile the information chromosome wise and

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]:

I would have thought that the file movement from one FS to another within the same pool would be almost instantaneous. Why does it take to platter for such a movement? # time cp /tmp/blockfile /pcshare/1gb-tempfile real 0m5.758s # time mv /pcshare/1gb-tempfile . real 0m4.501s Both FSs are with compression=off. /tmp is RAM. -devsk -- This message posted from opensolaris.org

I used screen scraping to extract some information and put it into a table called tbl. Now I want to modify the table a bit so the data can be more useful. Here's the code I used: library(XML) rm(list=ls()) url <- "http://webapp.montcopa.org/sherreal/salelist.asp?saledate=05/25/2011" tbl <-data.frame(readHTMLTable(url))[2:405, c(3,5,6,8,9)] names(tbl) <-

Hello I'm someone who whants to do a project using your vorbis libraries for encoding. I'm very impressed about what this codec can do and I want to use it. I have a school project in which I want to compress a wave file , using the power of many computers from a local network. So I'm asking you , if it is possible , to upload on your site (or send them to me , if i'm not asking

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,

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

I'm trying to use snow in my dual-core (hopefully later this is going to run in a cluster). So, at this moment I create a cluster using SOCK connection (MPI in the future). However when I try to use clusterApplyLB I got "Error in socketSelect(socklist) : not a socket connection". Any ideas ? Do you know if that is going to be an isuue too when I swith from SOCK to MPI ? Sample code

Looking at parLapplyLB, one sees that it takes in X and then passes splitList(X, length(cl)) to clusterApplyLB, which then calls dynamicClusterApply. Thus while dynamicClusterApply does handle tasks in a load-balancing fashion, sending out individual tasks as previous tasks complete, parLapplyLB preempts that by splitting up the tasks in advance into as many groups of tasks as there are cluster

Hello and thanks in advance for your time. I've created a simulation on my cluster which uses a custom package developed by me for different functions and also the snow package. Right now I'm using LAM to communicate between nodes and am currently only testing my code on 3 nodes for simplicity, though I plan on expanding to 16 later. My problem is this error: "Error in

Hi, I obtain continuosly segmentation fault of processes pop3 and imap, with version 2.2.10. This is the error for the pop3 process (but for imap is is the same): 2014-02-10 11:06:22 pop3(xxxxxxx at yyyyyyyy.zz): Fatal: master: service(pop3): child 23804 killed with signal 11 (core dumped) This is the kernel log: Feb 10 11:06:22 pecpop5 kernel: pop3[23804]: segfault at 7fa42a3f4070 ip

http://dovecot.org/releases/2.2/dovecot-2.2.13.tar.gz http://dovecot.org/releases/2.2/dovecot-2.2.13.tar.gz.sig A few minor changes since v2.2.13.rc1, mainly making the Pigeonhole tests pass. * Fixed a DoS attack against imap/pop3-login processes. If SSL/TLS handshake was started but wasn't finished, the login process attempted to eventually forcibly disconnect the client, but failed

http://dovecot.org/releases/2.2/dovecot-2.2.13.tar.gz http://dovecot.org/releases/2.2/dovecot-2.2.13.tar.gz.sig A few minor changes since v2.2.13.rc1, mainly making the Pigeonhole tests pass. * Fixed a DoS attack against imap/pop3-login processes. If SSL/TLS handshake was started but wasn't finished, the login process attempted to eventually forcibly disconnect the client, but failed

Dear R-Users, we are using a linux-cluster with RMPI and the snow package. We would like to do a double boostrap. We have a general function that implements the first boostrap (the outer) and we are wondering if we can include another bootstrap (the inner) in the same general function including another clusterApplyLB. For example: general function = function(...) { clusterApplyLB(cl,

Hey, i am now a bit deeper into dovecot debugging with gdb and have a full bt now, with debugging infos. Is there a patch that resolve this Problem in dovecot 2.2.22-1ubuntu2.13 for ubuntu 16? Here the bt: (gdb) bt full #0 0x00007ffff73cf3f3 in array_count_i (array=0x555555810d38) at ../../src/lib/array.h:155 No locals. #1 message_part_finish (ctx=ctx at entry=0x555555810ce0)

[cross-posted on R-devel and Bioc-devel, since the functions from the parallel package discussed here are mirrored in the BiocGenerics package] Hi, I am currently running a lengthy simulation study (no details necessary) on a large multi-core system. The simulated data sets are stored in a long list and they are unevenly sized (hence, the computation times vary greatly between data sets), so

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