[R-HPC] Cores hang when calling mcapply
I got errors like you show before. How much ram do you have access to on this server? My solution was to come up with a clean up line of code to remove old data from ram AND add an extra 256GB of ram. Drew Ekstrom Analytics Expert and Math Lecturer
From: R-sig-hpc <r-sig-hpc-bounces at r-project.org> on behalf of Deramus, Thomas Patrick <tderamus at mgb.org>
Sent: Wednesday, December 11, 2024 8:51:00 AM
To: r-sig-hpc at r-project.org <r-sig-hpc at r-project.org>
Subject: Re: [R-sig-hpc] [R-HPC] Cores hang when calling mcapply
Sent: Wednesday, December 11, 2024 8:51:00 AM
To: r-sig-hpc at r-project.org <r-sig-hpc at r-project.org>
Subject: Re: [R-sig-hpc] [R-HPC] Cores hang when calling mcapply
Follow up, this is the error I typically get:
Lost warning messages
Error: no more error handlers available (recursive errors?); invoking 'abort' restart
Execution halted
Error: cons memory exhausted (limit reached?)
Error: cons memory exhausted (limit reached?)
Error: cons memory exhausted (limit reached?)
Error: cons memory exhausted (limit reached?)
________________________________
From: R-sig-hpc <r-sig-hpc-bounces at r-project.org> on behalf of Deramus, Thomas Patrick <tderamus at mgb.org>
Sent: Wednesday, December 11, 2024 8:36 AM
To: r-sig-hpc at r-project.org <r-sig-hpc at r-project.org>
Subject: [R-sig-hpc] [R-HPC] Cores hang when calling mcapply
External Email - Use Caution
Hi R-HPC users.
I asked this on the main R-help list so apologies if you're seeing this again, but it was suggested I ask here as well
Additional apologies for the lack of concrete examples because the dataset is large, and it being so I believe is the issue.
I multiple, very large datasets for which I need to generate 0/1 absence/presence columns
Some include over 200M rows, with two columns that need presence/absence columns based on the strings contained within them, as an example, one set has ~29k unique values and the other with ~15k unique values (no overlap across the two).
Using a combination of custom functions:
crewjanitormakeclean <- function(df,columns) {
df <- df |> mutate(across(columns, ~make_clean_names(., allow_dupes = TRUE)))
return(df)
}
mass_pivot_wider <- function(df,column,prefix) {
df <- df |> distinct() |> mutate(n = 1) |> pivot_wider(names_from = glue("{column}"), values_from = n, names_prefix = prefix, values_fill = list(n = 0))
return(df)
}
sum_group_function <- function(df) {
df <- df |> group_by(ID_Key) |> summarise(across(c(starts_with("column1_name_"),starts_with("column2_name_"),), ~ sum(.x, na.rm = TRUE))) |> ungroup()
return(df)
}
and splitting up the data into a list of 110k individual dataframes based on Key_ID
temp <-
open_dataset(
sources = input_files,
format = 'csv',
unify_schema = TRUE,
col_types = schema(
"ID_Key" = string(),
"column1" = string(),
"column1" = string()
)
) |> as_tibble()
keeptabs <- split(temp, temp$ID_Key)
I used a multicore framework to distribute the `sum` functions across each Key_ID when a multicore argument is enabled.
??????
if(isTRUE(multicore)){
output <- mclapply(1:length(modtabs), function(i) crewjanitormakeclean(modtabs[[i]],c("string_columns_2","string_columns_1")), mc.cores = numcores)
output <- mclapply(1:length(modtabs), function(i) mass_pivot_wider(modtabs[[i]],"string_columns_1","col_1_name_"), mc.cores = numcores)
output <- mclapply(1:length(modtabs), function(i) mass_pivot_wider(modtabs[[i]],"string_columns_2","col_2_name_"), mc.cores = numcores)
}else{
output <- lapply(1:length(modtabs), function(i) crewjanitormakeclean(modtabs[[i]],c("string_columns_2","string_columns_1")))
output <- lapply(1:length(modtabs), function(i) mass_pivot_wider(modtabs[[i]],"string_columns_1","col_1_name_"))
output <- mclapply(1:length(modtabs), function(i) mass_pivot_wider(modtabs[[i]],"string_columns_2","col_2_name_"))
}
Moving every Key_ID to a single row and then row-binding the data while creating new columns for the differences across `Key_ID`s from the pivot using the following solution (78 upvotes at time of this email):
https://secure-web.cisco.com/1G1n4sFquFSAGXcAhqOo3c3lp6ipL3mpWrMcrJGlTpwl0u26UB5NiqsLAnhEOsrwLwZGKH9CM-7Xl2iReOmHOWwkbqfaa34jRl2zj6NG6ZPvF1Ev5GA4Yrquq9rXWECDdRRhl0d5KwSNbuM2pc8L6yTNU4MXRsptY-nQdIsDm_nqsvxEFKUE7Za9eyrt0_vkcGMQ596el4L50eQStz1JOCXuAjvKliwISQufp1-lyFdAkY-7MdIw59JGOY1oraDzYl937Vt-XX2r7Dx-v5KsF8VtyB0iMr_wQd5UA-tG8ZwdR8Q4pLKuoUuImGzteFCTK/https%3A%2F%2Fstackoverflow.com%2Fquestions%2F3402371%2Fcombine-two-data-frames-by-rows-rbind-when-they-have-different-sets-of-columns
allNms <- unique(unlist(lapply(keeptabs, names)))
output <- do.call(rbind,
c(lapply(keeptabs,
function(x) data.frame(c((x), sapply(setdiff(allNms, names(x)),
function(y) NA))) |> as_tibble()),
make.row.names=FALSE)) |> mutate(across(c(starts_with("column1_name_"), starts_with("column2_name_")), coalesce, 0))
However, I have noticed that the jobs seem to "hang" after a while, with the initial 30 or so (numcores == 30 in the workflow, equal to the number of cores I reserved) at 100% of the requested CPU, and then several "zombie" processes occur and the cores just stop at 0% and never proceed, usually dying with a timeout or not all jobs running to completion failure to join of some kind.
This happens in both base R and RStudio, and I haven't been able to figure out if it's something wrong with the code, the size of the data, or our architecture, but I would appreciate any suggestions as to what I might be able to do about this.
Before they are suggested, I have also tried this same approach with foreach, snow, future, and furr packages, and base parallel with mc.apply seems to be the only thing that works for at least one dataset.
In the event it has something to do with our architecture, here is what we are running on and our loaded packages:
OS Information:
NAME="Red Hat Enterprise Linux"
VERSION="9.3 (Plow)"
ID="rhel"
ID_LIKE="fedora"
VERSION_ID="9.3"
PLATFORM_ID="platform:el9"
PRETTY_NAME="Red Hat Enterprise Linux 9.3 (Plow)"
ANSI_COLOR="0;31"
LOGO="fedora-logo-icon"
CPE_NAME="cpe:/o:redhat:enterprise_linux:9::baseos"
HOME_URL="https://secure-web.cisco.com/1h7_cGThR4E8jHCYamL4o_FHW4nl7BFKMGMRVLQDdaZ0qWJ7A-AO5GfJfswTgIA64F6ti1IIn7r-uj5jO4fxVgTLzVnkMwvZAparKYNGPsv9N7h7nptYFHuoPa6ubPOWa-ptnh5DQr9JWYOwHrtJlvXZIUvxn6htl9SXhlBrJZPtfrD22_uTeb_TC1RG_qy1NjW7Mzye4iE86WIkQnA3bvCCFRBlHWGuBgOlFJgtA1pV3ikrH5CuKkFnUk6igeJp6qoszmYKA2mM-Z67tV7KU0rV4sFfBN4aJJcASSV7a7B0pYa3vwvFovlNsFgq5yTnY/https%3A%2F%2Fwww.redhat.com%2F"
DOCUMENTATION_URL="https://secure-web.cisco.com/1iWVumbt8ShswsWwWdSlpe9J8uvS5O_E5XF6SB08JYI2uBK0xwtlD_JkkSfg6D4XxoxmCj2zyMixuhtA6S8Xh-92U9udozSWm1efT79xD8hDmGxbK_qO4ZpGBu-5YjBRKom4_nQOl5SKgD5HQLS7xg9UPaeKYn94_CWiMScnd9BWHoquMx5XP8Ex-NLoBd0rElgWPFfx77yy6M8QWJ9yBsAc8WJD4E3Zt1oyt9CtOR52NGkG_YvLrxJrNqfluWrfFW6UJAzCX-zgH5JenNpyokC0Aq5Yce88oIbjyCsWISAZ7gMTCdWf1sHwS4wWj2M3M/https%3A%2F%2Faccess.redhat.com%2Fdocumentation%2Fen-us%2Fred_hat_enterprise_linux%2F9"
BUG_REPORT_URL="https://secure-web.cisco.com/1fNMHII_7nlcKfcWImleG3ZP7DT9eRQNmTSf3AkBzirmzCzp1zT3xBlJCfmy1hlrL0Rcn1Oz0DvMTU1qN_gRn4YRxxB7oSgAi7I1HJ6pMa76wdAyN7CHX7vqqS_U4ocSCDvUID48ibjS6kD7sKiwVZA0CnG5EJspkGrhwk6dlxxoeS0mrR1GkOF5CQ8VCfXzYwm80u_RLiLSNmb_udzjvNxHHXRbXupCU2goONv4sADdrVJeM7cxzDa30eRwSwC2F7hvsz0qmVU3rq3p5mNC376QI0ZgItffJ90fFP5uUxVl56Vs0XhD06muVstfxkVdl/https%3A%2F%2Fbugzilla.redhat.com%2F"
REDHAT_BUGZILLA_PRODUCT="Red Hat Enterprise Linux 9"
REDHAT_BUGZILLA_PRODUCT_VERSION=9.3
REDHAT_SUPPORT_PRODUCT="Red Hat Enterprise Linux"
REDHAT_SUPPORT_PRODUCT_VERSION="9.3"
Operating System: Red Hat Enterprise Linux 9.3 (Plow)
CPE OS Name: cpe:/o:redhat:enterprise_linux:9::baseos
Kernel: Linux 5.14.0-362.13.1.el9_3.x86_64
Architecture: x86-64
Hardware Vendor: Dell Inc.
Hardware Model: PowerEdge R840
Firmware Version: 2.15.1
R Version:
R.Version()
$platform
[1] "x86_64-pc-linux-gnu"
$arch
[1] "x86_64"
$os
[1] "linux-gnu"
$system
[1] "x86_64, linux-gnu"
$status
[1] ""
$major
[1] "4"
$minor
[1] "3.2"
$year
[1] "2023"
$month
[1] "10"
$day
[1] "31"
$`svn rev`
[1] "85441"
$language
[1] "R"
$version.string
[1] "R version 4.3.2 (2023-10-31)"
$nickname
[1] "Eye Holes"
RStudio Server Version:
RStudio 2023.09.1+494 "Desert Sunflower" Release (cd7011dce393115d3a7c3db799dda4b1c7e88711, 2023-10-16) for RHEL 9
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36
PPM Repo:
https://secure-web.cisco.com/1nMuCOnANj6fZMyl2-XZciqjw-DMAGy2kqdTnq_URbhzu9NQeVLtyJW9XYi-aDbPvK7JTLcN0pdEzLY7StrAcbkvQdfn9TbfGA9xdAaGuabqNLUHc6WQP7Lk5es-wAKt2tEf0GfTf4gkg6Lx22c2Xqjb-qUryZ5Fq_0XU2o-fmM9WK8CfLi352XNSlsW6M_sPP57zBgpSUc3vyy97jNOXMcw6f2pYhisuYLA9-EsIM-KQWohyQ_K83SKZ2wuKxRZ1vMEoRTUdOePklEGnQ2c7xBj-zxh5BxQ2I1jxs1pLC3wVq5iRlEJb-2TXB0knuHJD/https%3A%2F%2Fpackagemanager.posit.co%2Fcran%2F__linux__%2Frhel9%2Flatest
attached base packages:
[1] parallel stats graphics grDevices datasets utils methods base
other attached packages:
[1] listenv_0.9.1 microbenchmark_1.5.0 dbplyr_2.4.0 duckplyr_0.4.1 readxl_1.4.3 fastDummies_1.7.3
[7] glue_1.8.0 arrow_14.0.2.1 data.table_1.15.2 toolbox_0.1.1 janitor_2.2.0 lubridate_1.9.3
[13] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[19] tibble_3.2.1 ggplot2_3.5.0 tidyverse_2.0.0 duckdb_1.1.2 DBI_1.2.3 fs_1.6.3
Happy to provide additional information if it would be helpful.
Thank you in advance!
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