# Using RcppArmadillo with bigmemory

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The bigmemory package allows users to create

matrices that are external to R, stored either in RAM or on disk,

allowing them to be bigger than the system RAM, and allowing them to

be shared across R sessions.

While these objects are defined by the `big.matrix`

class in R, they

are really just wrappers that point to external memory. The actual

objects are implemented in C++, thus can be easily manipulated from

`Rcpp`

.

`Armadillo`

is a C++ library that provides fast linear algebra

functionality. It can be used in conjunction with `Rcpp`

through

the `RcppArmadillo`

package.

This example demonstrates how to use the functionality provided by

`Armadillo`

on `big.matrix`

objects using `Rcpp`

. To learn how to

work with `big.matrix`

objects in `Rcpp`

without using `RcppArmadillo`

see this previous gallery post.

Utilising the functions provided by `armadillo`

simply requires the

`Rcpp`

objects and `big.matrix`

objects to be case to the appropriate

types that `armadillo`

knows how to work with. To demonstrate this,

we again implement the equivalent of the `colSums`

function, but allow

the user to specify which columns they want to apply the function to

in advance.

// To enable the functionality provided by Armadillo's various macros, // simply include them before you include the RcppArmadillo headers. #define ARMA_NO_DEBUG #include// [[Rcpp::depends(RcppArmadillo, BH, bigmemory)]] using namespace Rcpp; using namespace arma; // The following header file provides the definitions for the BigMatrix // object #include // Bigmemory now accesses Boost headers from the BH package, // we need to make sure we do so as well in this Rcpp::depends comment. // Boost headers can generate some warning with the default compilation // options for R. To suppress these, we can enable C++11 mode which gets // us 'long long' types. // // If your compiler is to old, just disable / remove the following line // [[Rcpp::plugins(cpp11)]] // Implementation of colSums // // Because BigMatrix objects can come with four different underlying // types, we need to split our functionality into a "dispatch" function, // which makes the appropriate typecasts based on the matrix type, and an // "implementation" function which does the actual calculations. // template Col BigArmaColSums(const Mat & aBigMat, IntegerVector subsetCols) { // The functions for subsetting an arma::Mat expect the indices to // be in of type 'uvec' (shorthand for 'Col '), so we // need to conver the IntegerVector to that type. // // We subtract 1 to convert the R indices into C++ indices, since // indices in R start at 1, but in C++ they start at 0. uvec colidx = as (subsetCols) - 1; // To subset by columns, we use the .cols method for the arma::Mat // class. This allows us to access non-contiguous columns. return sum(aBigMat.cols(colidx)); } // Dispatch function for colSums // // We need to write this wrapper to handle the different types used // when constructing a big.matrix // // [[Rcpp::export]] NumericVector BigArmaColSums(SEXP pBigMat, IntegerVector subsetCols) { // First we tell Rcpp that the object we've been given is an external // pointer. XPtr xpMat(pBigMat); // First we should make sure that none of the requested columns are // outside of the provided matrix. If we let the code access an area // of memory it shouldn't, bad things will happen! if (is_true(any(subsetCols < 1)) || is_true(any(subsetCols > xpMat->ncol()))) { throw std::out_of_range("Some of requested columns are outside of the matrix!"); } // The actual data for the matrix is stored in the matrix() field. // This is just a pointer to an array, which is laid out in memory in // the column major format. Armadillo matrices are also stored in column // major format. We can therefore use the advanced `arma::mat` constructor // with `copy_aux_mem` set to `false` to effectively "cast" this memory // to an object RcppArmadillo understands. // // Note that this is an 'unsafe' cast, since we're telling armadillo // to use existing memory, rather than to create a new matrix. So we need // to be careful that the memory we're telling it to use has the correct // dimensions! // unsigned int type = xpMat->matrix_type(); // The data stored in the big.matrix can either be represent by 1, 2, // 4, or 8 bytes. See the "type" argument in `?big.matrix`. if (type == 1) { Col colSums = BigArmaColSums( arma::Mat ((char *)xpMat->matrix(), xpMat->nrow(), xpMat->ncol(), false), subsetCols ); return NumericVector(colSums.begin(), colSums.end()); } else if (type == 2) { Col colSums = BigArmaColSums( arma::Mat ((short *)xpMat->matrix(), xpMat->nrow(), xpMat->ncol(), false), subsetCols ); return NumericVector(colSums.begin(), colSums.end()); } else if (type == 4) { Col colSums = BigArmaColSums( arma::Mat ((int *)xpMat->matrix(), xpMat->nrow(), xpMat->ncol(), false), subsetCols ); return NumericVector(colSums.begin(), colSums.end()); } else if (type == 8) { Col colSums = BigArmaColSums( arma::Mat ((double *)xpMat->matrix(), xpMat->nrow(), xpMat->ncol(), false), subsetCols ); return NumericVector(colSums.begin(), colSums.end()); } else { // We should never get here, but it resolves compiler warnings. throw Rcpp::exception("Undefined type for provided big.matrix"); } }

So lets see how this function performs in comparison to `colSums`

,

BigColSums.

library(microbenchmark) library(bigmemory) Loading required package: methods Loading required package: bigmemory.sri Loading required package: BH bigmemory >= 4.0 is a major revision since 3.1.2; please see packages biganalytics and and bigtabulate and http://www.bigmemory.org for more information. # Lets test the performance on 100 random columns from a 60*1000 matrix. set.seed(4) ridx <- sample(1:1000, 100) m <- matrix(rnorm(60*1000), nrow=60) bigm <- as.big.matrix(m) options(width=100) # Make sure output looks ok microbenchmark(res1 <- colSums(m[,ridx]), res2 <- BigArmaColSums([email protected], ridx)) Unit: microseconds expr min lq median uq max neval res1 <- colSums(m[, ridx]) 56.24 57.98 59.13 75.12 1068.41 100 res2 <- BigArmaColSums([email protected], ridx) 12.48 13.84 19.53 36.55 61.63 100 # and make sure the results are the same: all.equal(res1, res2) [1] TRUE

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