I have a function that computes basic summary statistics from the rows (or columns) of a given Matrix and I am now trying to also use this function with a bigstatsr::FBM (I am aware that using columns should be more efficient). The reason I want to store the rows / columns in a vector is that I would like to compute quantiles with std::nth_element. If there is a different way to do that with out the vector I would be equally happy.
This is the code I use for a regular matrix.
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::depends(RcppEigen)]]
#include <RcppEigen.h>
using namespace Rcpp;
// [[Rcpp::export]]
Eigen::MatrixXd summaryC(Eigen::MatrixXd x,int nrow) {
Eigen::MatrixXd result(nrow, 5);
int indices[6] = {-1, 0, 249, 500, 750, 999};
for (int i = 0; i < nrow; i++) {
Eigen::VectorXd v = x.row(i);
for (int q = 0; q < 5; ++q) {
std::nth_element(v.data() + indices[q] + 1,
v.data() + indices[q+1],
v.data() + v.size());
result(i,q) = v[indices[q+1]];
}
}
return result;
}
/*** R
x <- matrix(as.numeric(1:1000000), ncol = 1000)
summaryC(x = x, nrow = 1000)
***/
However I struggle to do this with an FBM as I am not fully grasping the intricacies of how the FBM - Pointer works.
I tried the following without success:
// [[Rcpp::depends(BH, bigstatsr, RcppEigen)]]
// [[Rcpp::plugins(cpp11)]]
#include <bigstatsr/BMAcc.h>
#include <RcppEigen.h>
// [[Rcpp::export]]
Eigen::MatrixXd summaryCbig(Environment fbm,int nrow, Eigen::VecttorXi ind_col) {
Eigen::MatrixXd result(nrow, 5);
XPtr<FBM> xpMat = fbm["address"];
BMAcc<double> macc(xpMat);
int indices[6] = {-1, 0, 249, 500, 750, 999};
for (int i = 0; i < nrow; i++) {
Eigen::VectorXd v = macc.row(i); // this does not work
Eigen::VectorXd v = macc(i,_); // this does not work
SubBMAcc<double> maccr(XPtr, i, ind_col -1); // This did not work with Eigen::VectorXi, but works with const NumericVector&
Eigen::VectorXd v = maccr // this does not work even for appropriate ind_col
for (int q = 0; q < 5; ++q) {
std::nth_element(v.data() + indices[q] + 1,
v.data() + indices[q+1],
v.data() + v.size());
macc(i,q) = v[indices[q+1]];
}
}
}
/*** R
x <- matrix(as.numeric(1:1000000), ncol = 1000)
summaryCbig(x = x, nrow = 1000, ind_col = 1:1000)
***/
Any help would be greatly appreciated, thank you!
Update - the big_apply - approach
I implemented the approach twice with two differently sized matrices X1 and X2. Code for X1:
X1 <- FBM(1000, 1000, init 1e6)
X2 <- FBM(10000, 10000, init = 9999)
library(bigstatsr)
microbenchmark::microbenchmark(
big_apply(X, a.FUN = function(X, ind) {
matrixStats::rowQuantiles(X1[ind, ])
}, a.combine = "rbind", ind = rows_along(X), ncores = nb_cores(), block.size = 500),
big_apply(X, a.FUN = function(X, ind) {
matrixStats::rowQuantiles(X1[ind, ])
}, a.combine = "rbind", ind = rows_along(X), ncores = 1, block.size = 500),
times = 5
)
When using X1 and block.size = 500, having 4 cores instead of 1 makes the task 5-10 times slower on my PC (4 CPU and using windows, unfortunately). using the bigger matrix X2 and leaving block.size with the default takes 10 times longer with 4 cores instead of the non-parallelized version.
Result for X2:
min lq mean median uq max neval
16.149055 19.13568 19.369975 20.139363 20.474103 20.951676 5
1.297259 2.67385 2.584647 2.858035 2.867537 3.226552 5
Assuming you have
library(bigstatsr)
X <- FBM(1000, 1000, init = 1:1e6)
I would not reinvent the wheel and use:
big_apply(X, a.FUN = function(X, ind) {
matrixStats::rowQuantiles(X[ind, ])
}, a.combine = "rbind", ind = rows_along(X), ncores = nb_cores(), block.size = 500)
Choose the block.size (number of rows) wisely.
Function big_apply() is very useful if you want to apply an R(cpp) function to blocks of the FBM.
Edit: Of course, parallelism will me slower for small matrices, because of OVERHEAD of parallelism (usually, 1-3 seconds). See the results for X1 and X2:
library(bigstatsr)
X1 <- FBM(1000, 1000, init = 1e6)
microbenchmark::microbenchmark(
PAR = big_apply(X1, a.FUN = function(X, ind) {
matrixStats::rowQuantiles(X[ind, ])
}, a.combine = "rbind", ind = rows_along(X1), ncores = nb_cores(), block.size = 500),
SEQ = big_apply(X1, a.FUN = function(X, ind) {
matrixStats::rowQuantiles(X[ind, ])
}, a.combine = "rbind", ind = rows_along(X1), ncores = 1, block.size = 500),
times = 5
)
Unit: milliseconds
expr min lq mean median uq max neval cld
PAR 1564.20591 1602.0465 1637.77552 1629.9803 1651.04509 1741.59974 5 b
SEQ 68.92936 69.1002 76.70196 72.9173 85.31751 87.24543 5 a
X2 <- FBM(10000, 10000, init = 9999)
microbenchmark::microbenchmark(
PAR = big_apply(X2, a.FUN = function(X, ind) {
matrixStats::rowQuantiles(X[ind, ])
}, a.combine = "rbind", ind = rows_along(X2), ncores = nb_cores(), block.size = 500),
SEQ = big_apply(X2, a.FUN = function(X, ind) {
matrixStats::rowQuantiles(X[ind, ])
}, a.combine = "rbind", ind = rows_along(X2), ncores = 1, block.size = 500),
times = 5
)
Unit: seconds
expr min lq mean median uq max neval cld
PAR 4.757409 4.958869 5.071982 5.083381 5.218098 5.342153 5 a
SEQ 10.842828 10.846281 11.177460 11.360162 11.416967 11.421065 5 b
The bigger your matrix is, the more you will gain from parallelism.