Closed fbnrst closed 3 years ago
So I understand the issue, you want only 4 cores to be used, but instead how many appear to be used? Can you provide the exact set of commands you are running.
On Mar 24, 2021, at 3:49 PM, Fabian Rost @.***> wrote:
Thanks for creating MAST!
For me, MAST always uses all cores when running zlm. I did set "options("mc.cores"=4)" and I also specified
zlm(..., parallel = FALSE) Still, when I run zlm all cores of my machine are used. Is there another option I do have to set?
R version 4.0.3 (2020-10-10) Platform: x86_64-conda-linux-gnu (64-bit) Running under: Debian GNU/Linux 10 (buster)
Matrix products: default BLAS/LAPACK: /opt/conda/lib/libmkl_gf_lp64.so
locale: [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=Cattached base packages: [1] parallel stats4 tools stats graphics grDevices utils
[8] datasets methods baseother attached packages: [1] MAST_1.16.0 SingleCellExperiment_1.12.0 [3] SummarizedExperiment_1.20.0 Biobase_2.50.0
[5] GenomicRanges_1.42.0 GenomeInfoDb_1.26.0
[7] IRanges_2.24.0 S4Vectors_0.28.0
[9] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[11] matrixStats_0.58.0loaded via a namespace (and not attached): [1] Rcpp_1.0.6 plyr_1.8.6 compiler_4.0.3
[4] pillar_1.5.1 XVector_0.30.0 bitops_1.0-6
[7] zlibbioc_1.36.0 lifecycle_1.0.0 tibble_3.1.0
[10] gtable_0.3.0 lattice_0.20-41 pkgconfig_2.0.3
[13] rlang_0.4.10 Matrix_1.3-2 DBI_1.1.1
[16] DelayedArray_0.16.0 GenomeInfoDbData_1.2.4 dplyr_1.0.5
[19] stringr_1.4.0 generics_0.1.0 vctrs_0.3.6
[22] tidyselect_1.1.0 grid_4.0.3 glue_1.4.2
[25] data.table_1.14.0 R6_2.5.0 fansi_0.4.2
[28] reshape2_1.4.4 purrr_0.3.4 ggplot2_3.3.3
[31] magrittr_2.0.1 scales_1.1.1 ellipsis_0.3.1
[34] assertthat_0.2.1 abind_1.4-5 colorspace_2.0-0
[37] utf8_1.1.4 stringi_1.5.3 RCurl_1.98-1.2
[40] munsell_0.5.0 crayon_1.4.1
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exactly, this is what I wanted. Now, I tried to reproduce the issue, but I did some updates in between and I think it is gone. Thanks!
I have this very same problem, with MAST 1.26.0. I'm on a shared machine and it is literally taking up all. I guess some detectCores might be hidden/hardcoded somewhere? Even if I specify parallel = FALSE it still uses all it finds.
options(mc.cores = 4)
zlmCond <- zlm( ~ broad_cell_type + cngeneson, sca, parallel = TRUE)
saveRDS(zlmCond, "MAST_zlm.RDS")
Please send sessionInfo() and an example that can be run from a fresh R session.
On Fri, Jun 16, 2023 at 1:32 AM rdacemel @.***> wrote:
I have this very same problem, with MAST 1.26.0. I'm on a shared machine and it is literally taking up all. I guess some detectCores might be hidden/hardcoded somewhere? Even if I specify parallel = FALSE it still uses all it finds.
options(mc.cores = 4) zlmCond <- zlm( ~ broad_cell_type + cngeneson, sca, parallel = FALSE) writeRDS(zlmCond, "MAST_zlm.RDS", parallel = TRUE)
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I think I figured something out. The multicore is working fine, but something underneath is trying to multithread as much as it can. I "fixed" it with:
library(RhpcBLASctl)
blas_set_num_threads(4)
Then, if I parallel with 4 it uses 4 x 4 = 16 threads in total. I don't know exactly what this blas is doing or which package is calling it...
Thanks for creating MAST!
For me, MAST always uses all cores when running
zlm
. I did set "options("mc.cores"=4)" and I also specifiedStill, when I run
zlm
all cores of my machine are used. Is there another option I do have to set?