similar to: Another issue using multi-processing linear algebra libraries

? Tue, 21 May 2024 08:00:11 +0000 Dario Strbenac via R-devel <r-devel at r-project.org> ?????: > Would a less resource-intensive value, such as 1, be a safer default > CPU value for confint? Which confint() method do you have in mind? There is at least four of them by default in R, and many additional classes could make use of stats:::confint.default by implementing vcov(). >

From the R Installation and Admin manual: "There is a tendency for re-distributors of R to use ?enhanced? linear algebra libraries without explaining their downsides.? There?s a downside not mentioned in the manual that caught and baffled me for a while. I was using all 64 cores of an AWS instance via parallel::mclapply() and doing matrix multiplications in the parallelized function. If

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

On 10/11/23 23:12, George Ostrouchov wrote: > I agree that the "R Installation and Administration" guide statement about FlexiBLAS needs an update. Standard R in CentOS Stream 9, which is now downstream from Fedora, gives the following result for Matrix products in sessionInfo(): > >> sessionInfo() > R version 4.3.1 (2023-06-16) > Platform: x86_64-redhat-linux-gnu

Hello, Would a less resource-intensive value, such as 1, be a safer default CPU value for confint? I noticed excessive CPU usage on a I.T. administrator-managed server which was being three-quarters used by another staff member when the confidence interval calculation in an R Markdown document suddenly changed from two seconds to ninety seconds because of competition for CPUs between users. Also,

Hi all, Henrik Bengtsson has done some fantastic work with {future} and, more importantly, greatly improved constructing and deconstructing a parallelized environment within R. It was with great joy that I saw Henrik slowly split off some functionality of {future} into {parallelly} package. Reading over the package?s README, he states: > The functions and features added to this package are

I agree that the "R Installation and Administration" guide statement about FlexiBLAS needs an update. Standard R in CentOS Stream 9, which is now downstream from Fedora, gives the following result for Matrix products in sessionInfo(): > sessionInfo() R version 4.3.1 (2023-06-16) Platform: x86_64-redhat-linux-gnu (64-bit) Running under: CentOS Stream 9 Matrix products: default

FWIW, there are indeed a few low hanging bug fixes in 'parallelly' that should be easy to incorporate into 'parallel' without adding extra maintenance. For example, in parallel::makePSOCKcluster(), it is not possible to disable SSH option '-l USER' so that it can be set in ~/.ssh/config. The remote user name will be the user name of your local machine and if you try to

Following up on this -- on my system, I have 69 packages installed that appear to provide something like a confint() method: h <- help.search("confint", agrep = FALSE) p <- sort(unique(h$matches$Package)) length(p) ## [1] 69 p [1] "bamlss" "bbmle" "binom" "brglm2" [5] "broom"

Hi Ivan, An apology, I was away for quite a bit. To reproduce the setup: I have been using the default GCC in RHEL 9.1. gcc -v Using built-in specs. COLLECT_GCC=gcc COLLECT_LTO_WRAPPER=/usr/libexec/gcc/x86_64-redhat-linux/11/lto-wrapper OFFLOAD_TARGET_NAMES=nvptx-none OFFLOAD_TARGET_DEFAULT=1 Target: x86_64-redhat-linux Configured with: ../configure --enable-bootstrap --enable-host-pie

I've not worked changing underlying computational infrastructure, but developers who do might want to use ideas from FlexiBLAS. Apologies in advance if this is well-known. Best JN > From: Martin Koehler koehlerm at mpi-magdeburg.mpg.de > Date: January 07, 2016 > Subject: FlexiBLAS Version 1.3.0 Release > > It is our pleasure to announce the new release of FlexiBLAS. We do not

Could you recommend any packages/functions that compute hash such that the source references and sexpinfo_struct are ignored? Basically a version of `serialize` that convert R objects to raw without storing the ancillary source reference and sexpinfo. I think most people would think of `digest` but that package uses `serialize` (see discussion

Dear R devs, I was digging into a package issue today when I realized R serialize function not always generate the same results on equivalent objects when users choose to run differently. For example, the following code serialize(with(new.env(), { function(){} }), NULL, TRUE) generates different results when I copy-paste into console vs when I use ctrl+shift+enter to source the file in RStudio.

> "There?s a downside not mentioned in the manual that caught and baffled me for a while. I was using all 64 cores of an AWS instance via parallel::mclapply() and doing matrix multiplications in the parallelized function. If the matrices were big enough the linked BLAS or LAPACK would try to use all 64 cores for each multiplication, which meant 64^2 processes or threads in some combination

On 1/16/24 20:16, Dipterix Wang wrote: > Could you recommend any packages/functions that compute hash such that > the source references and sexpinfo_struct are ignored? Basically a > version of `serialize` that convert R objects to raw without storing > the ancillary source reference and sexpinfo. > I think most people would think of `digest` but that package uses >

Seems like this in 83986 [1] needs a fix in Lapack.c: if (dladdr((void *) F77_NAME(ilaver), &dl_info)) { char buf[PATH_MAX+1]; char *res = realpath(dl_info.dli_fname, buf); if (res) { SEXP nfo = R_NilValue; if (strstr(res, "flexiblas")) nfo = R_flexiblas_info(); if

Hi all, I am using R-3.0.1 under Linux platform to embed R into my C++ code. I am facing an error while executing more than 1 R-expressions parallelly. I am executing round(X) and abs(X) parallelly on a set of 50 input rows which resulted in segmentation fault after getting the following errors. Error: unprotect_ptr: pointer not found Error: argument to 'findVar' is not an environment

I am looking for ways to add points to three different plots in parallel. I generate three scatter plots and name them as s3d1, s3d2 and s3d3 s3d1<-scatterplot3d(mtcars[,3],mtcars[,4],mtcars[,5],main="common",pch=20) s3d2<-scatterplot3d(mtcars[,3],mtcars[,4],mtcars[,5],main="common",pch=20)

? Tue, 16 Jan 2024 14:16:19 -0500 Dipterix Wang <dipterix.wang at gmail.com> ?????: > Could you recommend any packages/functions that compute hash such > that the source references and sexpinfo_struct are ignored? Basically > a version of `serialize` that convert R objects to raw without > storing the ancillary source reference and sexpinfo. I can show how this can be done, but

Hi, R has been built against FlexiBLAS in rawhide [1, 2]. These are the R packages that need to be rebuilt: R-ape-0:5.4-2.fc33.x86_64 R-expm-0:0.999.4-7.fc33.x86_64 R-gee-0:4.13.20-4.fc33.x86_64 R-gss-0:2.2.2-3.fc33.x86_64 R-igraph-0:1.2.5-3.fc33.x86_64 R-msm-0:1.6.8-5.fc33.x86_64 R-preprocessCore-0:1.50.0-4.fc33.x86_64 R-qtl-0:1.46.2-4.fc33.x86_64 R-quadprog-0:1.5.8-5.fc33.x86_64 Could