Last updated: 2021-10-22

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When the number of variants is large, there is a huge memory usage when summarizing the CSs. Here is an illustration.

Suppose there are 12,000 variants, and we simulate under the null. We fit the model using SuSiE-RSS. The memory usage problem is caused by the huge number of variants in the CSs. To reproduce the problem, we fit the model with fixed prior variance. In the case with estimated prior variance, the problem occurs when the estimated prior variance is small and the 95% CS contains a large number of variants.

p = 12000
n = 1000
set.seed(1)
X = matrix(rnorm(n*p), n, p)
y = rnorm(n)
ss = susieR:::univariate_regression(X,y)
z = ss$betahat/ss$sebetahat
R = cor(X)

The LD matrix takes 1.2 GB.

Model profiling for susieR 0.11.52 (the version we’ve used for a long time).

devtools::install_github('stephenslab/susieR@d0965b8da322e098d7e3f376de99d5e9684c3d3a', force=TRUE)
# restart R session
library(profvis)
profvis({
  f <- susieR::susie_rss(z, R, estimate_prior_variance = FALSE)
})

I attached a screenshot from the profile.

The is_symmetric_matrix, is.na use a lot memory. Moreover, the mean and median from get_purity use a lot memory.

The code related to mean and median in get_purity are

value = abs(Xcorr[pos,pos])
mean(value,na.rm = TRUE)
median(value,na.rm = TRUE)

The value is a symmetric matrix. We compute the mean and median for the whole matrix, which use those off-diagonal elements twice! It seems like the upper triangular of value is enough to compute mean and median. So we change the code to the following, which uses Rcpp code from Rfast.

value = abs(Rfast::upper_tri(Xcorr[pos, pos], diag = TRUE))
sum(value,na.rm = TRUE)/sum(!is.na(value)) # mean
Rfast::med(value) # median
devtools::install_github('stephenslab/susieR@445013a0f40073973d390b6b0691f9971bf59131', force=TRUE)
# restart R session
library(profvis)
profvis({
  f <- susieR::susie_rss(z, R, estimate_prior_variance = FALSE)
})

The memory usage improves a lot, but it’s still using about 17GB. Getting upper triangular matrix, median, is_symmetric_matrix, is.na, abs use large memory.


sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7        whisker_0.4       knitr_1.33        magrittr_2.0.1   
 [5] R6_2.5.1          rlang_0.4.12      fansi_0.5.0       stringr_1.4.0    
 [9] tools_4.1.0       xfun_0.24         utf8_1.2.2        git2r_0.28.0     
[13] htmltools_0.5.1.1 ellipsis_0.3.2    rprojroot_2.0.2   yaml_2.2.1       
[17] digest_0.6.28     tibble_3.1.5      lifecycle_1.0.1   crayon_1.4.1     
[21] later_1.2.0       vctrs_0.3.8       promises_1.2.0.1  fs_1.5.0         
[25] glue_1.4.2        evaluate_0.14     rmarkdown_2.9     stringi_1.7.3    
[29] compiler_4.1.0    pillar_1.6.4      httpuv_1.6.1      pkgconfig_2.0.3