beachmat 2.23.6
The beachmat package provides a C++ API to extract numeric data from matrix-like R objects, based on the matrix representations in the tatami library. This enables Bioconductor packages to use C++ for high-performance processing of data in arbitrary R matrices, including:
matrix objectsDelayedMatrix objects with delayed operations from the DelayedArray packageDelayedArray::extract_array() method.Where possible, beachmat will map the R object to its C++ representation, bypassing the R interpreter to directly extract the matrix contents. This provides fast access by avoiding R-level evaluation, saves memory by avoiding block processing and memory allocations, and permits more fine-grained parallelization. For objects without native support, the R interpreter is called in a thread-safe manner to ensure that any downstream C++ code still works.
Packages that use beachmat’s API should set the following:
The compiler should support the C++17 standard.
Developers need to tell the build system to use C++17, by modifying the SystemRequirements field of the DESCRIPTION file:
SystemRequirements: C++17… or modifying the PKG_CPPFLAGS in the Makevars with the relevant flags.
Rcpp should be installed.
Developers need to ensure that Rcpp is loaded when the package is loaded.
This requires addition of Rcpp to the Imports field of the DESCRIPTION file:
Imports: Rcpp… and a corresponding importFrom specification in the NAMESPACE file:
importFrom(Rcpp, sourceCpp)(The exact function to be imported doesn’t matter, as long as the namespace is loaded. Check out the Rcpp documentation for more details.)
Linking to beachmat is as simple as writing:
LinkingTo: Rcpp, assorthead, beachmat… in the DESCRIPTION file.
Given an arbitrary matrix-like object, we create its C++ representation using the initializeCpp() function.
This process is very cheap as no data is copied, so the C++ object only holds views on the memory allocated and owned by R itself.
# Mocking up some kind of matrix-like object.
library(Matrix)
x <- round(rsparsematrix(1000, 10, 0.2))
# Initializing it in C++.
library(beachmat)
ptr <- initializeCpp(x)ptr now refers to a BoundNumericMatrix object, the composition of which can be found in the Rtatami.h header.
Of particular relevance is the ptr member, which contains a pointer to a tatami::NumericMatrix object derived from the argument to initializeCpp().
Developers can read the documentation in the header file for more details:
browseURL(system.file("include", "Rtatami.h", package="beachmat"))Now we can write a function that uses tatami to operate on ptr.
All functionality described in tatami’s documentation can be used here;
the only beachmat-specific factor is that developers should include the Rtatami.h header first (which takes care of the tatami headers).
Let’s just say we want to compute column sums - a simple implementation might look like this:
#include "Rtatami.h"
#include <vector>
#include <algorithm>
// Not necessary in a package context, it's only used for this vignette:
// [[Rcpp::depends(beachmat, assorthead)]]
// [[Rcpp::export(rng=false)]]
Rcpp::NumericVector column_sums(Rcpp::RObject initmat) {
Rtatami::BoundNumericPointer parsed(initmat);
const auto& ptr = parsed->ptr;
auto NR = ptr->nrow();
auto NC = ptr->ncol();
std::vector<double> buffer(NR);
Rcpp::NumericVector output(NC);
auto wrk = ptr->dense_column();
for (int i = 0; i < NC; ++i) {
auto extracted = wrk->fetch(i, buffer.data());
output[i] = std::accumulate(extracted, extracted + NR, 0.0);
}
return output;
}Let’s compile this function with Rcpp and put it to work.
We can just pass in the ptr that we created earlier:
column_sums(ptr)## [1] 26 15 0 3 1 1 -6 -13 -31 -1In general, we suggest calling initializeCpp() within package functions rather than asking users to call it themselves.
The external pointers should never be exposed to the user, as they do not behave like regular objects, e.g., they are not serializable.
Fortunately, the initializeCpp() calls are very cheap and can be performed at the start of any R function that needs to do matrix operations in C++.
tatami calls are normally thread-safe, but if the tatami::NumericMatrix is constructed from an unsupported object, it needs to call R to extract the matrix contents.
The R interpreter is strictly single-threaded, which requires some care when defining our chosen parallelization scheme.
The easiest way to achieve parallelization is to use the tatami::parallelize() function:
#include "Rtatami.h"
#include <vector>
#include <algorithm>
// Not necessary in a package context, it's only used for this vignette:
// [[Rcpp::depends(beachmat, assorthead)]]
// [[Rcpp::export(rng=false)]]
Rcpp::NumericVector parallel_column_sums(Rcpp::RObject initmat, int nthreads) {
Rtatami::BoundNumericPointer parsed(initmat);
const auto& ptr = parsed->ptr;
auto NR = ptr->nrow();
auto NC = ptr->ncol();
Rcpp::NumericVector output(NC);
tatami::parallelize([&](int thread, int start, int length) {
std::vector<double> buffer(NR);
auto wrk = ptr->dense_column();
for (int i = start, end = start + length; i < end; ++i) {
auto extracted = wrk->fetch(i, buffer.data());
output[i] = std::accumulate(extracted, extracted + NR, 0.0);
}
}, NC, nthreads);
return output;
}Now we put it to work with 2 threads.
Note that tatami::parallelize() (or specifically, the macros in Rtatami.h) will use the standard <thread> library to handle parallelization,
so it will work even on toolchains that do not have OpenMP support.
parallel_column_sums(ptr, 2)## [1] 26 15 0 3 1 1 -6 -13 -31 -1More advanced users can check out the parallelization-related documentation in the tatami_r repository.
The conventional approach to iterating over a matrix is to use DelayedArray’s block processing mechanism, i.e., DelayedArray::blockApply.
This is implemented completely in R and is convenient for developers as it can be used directly with any R function.
However, its performance only goes so far, and several years of experience with its use has revealed a few shortcomings:
Shifting the iteration into C++ with beachmat avoids many of these issues.
The looping overhead is effectively eliminated as the R interpreter is no longer involved.
Only one row/column is extracted at a time for most tatami::Matrix classes, minimizing memory overhead and avoiding the need for manual block size management in most cases.
Parallelization is also much easier with the standard <thread> library, as we do not need to spin up or fork to create a separate process.
sessionInfo()## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] beachmat_2.23.6 Matrix_1.7-1 knitr_1.49 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] crayon_1.5.3 cli_3.6.3 rlang_1.1.4
## [4] xfun_0.49 generics_0.1.3 jsonlite_1.8.9
## [7] DelayedArray_0.33.3 S4Vectors_0.45.2 htmltools_0.5.8.1
## [10] MatrixGenerics_1.19.0 sass_0.4.9 stats4_4.5.0
## [13] rmarkdown_2.29 grid_4.5.0 abind_1.4-8
## [16] evaluate_1.0.1 jquerylib_0.1.4 fastmap_1.2.0
## [19] IRanges_2.41.2 yaml_2.3.10 lifecycle_1.0.4
## [22] bookdown_0.41 BiocManager_1.30.25 compiler_4.5.0
## [25] Rcpp_1.0.13-1 XVector_0.47.1 lattice_0.22-6
## [28] digest_0.6.37 R6_2.5.1 SparseArray_1.7.2
## [31] assorthead_1.1.8 bslib_0.8.0 tools_4.5.0
## [34] matrixStats_1.4.1 S4Arrays_1.7.1 BiocGenerics_0.53.3
## [37] cachem_1.1.0