beachmat 2.0.0
This document describes the use of the beachmat API for accessing data in R matrices. We will demonstrate the API on numeric matrices, though same semantics are used for matrices of other types (e.g., logical, integer, character). First, we include the relevant header file:
#include "beachmat/numeric_matrix.h"A double-precision matrix object dmat is handled in C++ by passing the SEXP struct from .Call to create_numeric_matrix:
auto dptr = beachmat::create_numeric_matrix(dmat);This creates a unique pointer that points to an object of the numeric_matrix base class.
The exact derived class that is actually instantiated depends on the type of matrix in dmat, though the behaviour of the user-level functions are not affected by this detail.
Additional notes
auto keyword just avoids the need to write the full type of the returned pointer, which is std::unique_ptr<beachmat::numeric_matrix>.
We use unique pointers to control ownership and smoothly handle destruction and memory deallocation at the end of the function.std::exception class, containing an informative error message.
These should be caught and handled gracefully by the end-user code, otherwise a segmentation fault will probably occur.
See the error-handling mechanism in Rcpp for how to deal with these exceptions.The get_nrow() method returns the number of rows in the matrix:
size_t nrow = dptr->get_nrow();The get_ncol() method returns the number of columns in the matrix:
size_t ncol = dptr->get_ncol();The get_class() method returns the class of the matrix representation pointed to by dptr,
while the get_package() method returns the package in which that class is defined1 In case two packages define the same class name..
std::string mat_type = dptr->get_class();The yield() method returns the original R matrix that was used to create dptr.
Rcpp::RObject original = dptr->yield();The get_col() method fills an iterator in to an Rcpp vector with values from a column c of the matrix.
There should be at least nrow accessible elements, i.e., *in and *(in+nrow-1) should be valid entries.
dptr->get_col(
c, /* size_t */
in /* Rcpp::Vector::iterator */
);Extraction of a range of the column can be specified with the first and last arguments.
This will fill in with values at column c from row first to last-1.
There should be at least last-first accessible elements, i.e., *in and *(in+last-first-1) should be valid entries.
dptr->get_col(
c, /* size_t */
in, /* Rcpp::Vector::iterator */
first, /* size_t */
last /* size_t */
);No value is returned by either of these methods.
Note that c should be a zero-indexed integer in [0, ncol).
Similarly, both first and last should be in [0, nrow] and zero-indexed, with the additional requirement that last >= first.
The get_row() method takes an iterator in to a Rcpp vector and fills it with values at row r.
There should be at least ncol accessible elements, i.e., *in and *(in+ncol-1) should be valid entries.
dptr->get_row(
r, /* size_t */
in /* Rcpp::Vector::iterator */
);Extraction of a range of the row can be specified with the first and last arguments.
This will fill in with values at row r from column first to last-1.
There should be at least last-first accessible elements, i.e., *in and *(in+last-first-1) should be valid entries.
dptr->get_row(
r, /* size_t */
in, /* Rcpp::Vector::iterator */
first, /* size_t */
last /* size_t */
);No value is returned by either of these methods.
Again, r should be a zero-indexed integer in [0, nrow).
Both first and last should be in [0, ncol] and zero-indexed, with the additional requirement that last >= first.
The get() method returns a double-precision value at the matrix entry for row r and column c.
Both r and c should be zero-indexed integers in [0, nrow) and [0, ncol) respectively.
double val = dptr->get(
r, /* size_t */
c /* size_t */
);If the object in is a Rcpp::NumericVector::iterator instance, matrix entries will be extracted as double-precision values.
If it is a Rcpp::IntegerVector::iterator instance, matrix entries will be extracted as integers with implicit conversion from the double-precision type in dptr.
It is also possible to use a Rcpp::LogicalVector::iterator, though see the warnings below.
The get_cols() method fills an iterator in to an Rcpp vector with values from multiple columns of the matrix.
The idx iterator should point to an array of integers of length n, containing the column indices to use for extraction.
The indices should be zero-based and strictly increasing, i.e., no duplicates.
dptr->get_cols(
idx, /* Rcpp::IntegerVector::iterator */
n, /* size_t */
in, /* Rcpp::Vector::iterator */
first, /* size_t */
last /* size_t */
);For each column, the range of values in [first, last) are extracted.
If first and last are not specified, the range will default to [0, nrow).
Thus, there should be at least n*(last-first) accessible elements pointed to by in.
This method will extract values in column-major format.
That is, if one were to compute a submatrix containing the selected columns and the chosen row range, that submatrix would be available in column-major form in in.
No value is returned by this method.
The get_rows() method fills an iterator in to an Rcpp vector with values from multiple rows of the matrix.
The idx iterator should point to an array of integers of length n, containing the column indices to use for extraction.
The indices should be zero-based and strictly increasing, i.e., no duplicates.
dptr->get_rows(
idx, /* Rcpp::IntegerVector::iterator */
n, /* size_t */
in, /* Rcpp::Vector::iterator */
first, /* size_t */
last /* size_t */
);For each row, the range of values in [first, last) are extracted.
If first and last are not specified, the range will default to [0, ncol).
Thus, there should be at least n*(last-first) accessible elements pointed to by in.
Like get_cols(), this method will extract values in column-major format.
That is, if one were to compute a submatrix containing the selected columns and the chosen row range, that submatrix would be available in column-major form in in.
Note that this means that contiguous elements in in are not from the same row!
Rather, they will be from the same column, but only from the rows specified by idx.
No value is returned by this method.
To create logical, integer and character matrices, include the following header files:
#include "beachmat/logical_matrix.h"
#include "beachmat/integer_matrix.h"
#include "beachmat/character_matrix.h"The dispatch function changes correspondingly for logical matrix lmat, integer matrix imat or character matrix cmat.
Each function creates a unique pointer to a *_matrix of the appropriate type.
// creates a std::unique_ptr<beachmat::logical_matrix>
auto lptr=beachmat::create_logical_matrix(lmat);
// creates a std::unique_ptr<beachmat::integer_matrix>
auto iptr=beachmat::create_integer_matrix(imat);
// creates a std::unique_ptr<beachmat::character_matrix>
auto cptr=beachmat::create_character_matrix(cmat);Equivalent methods are available for each matrix type with appropriate changes in type.
For integer and logical matrices, get() will return an integer.
in can be any type previously described for numeric_matrix objects.
For character matrices, all iterators should be of type Rcpp::StringVector::iterator, and get() will return a Rcpp::String.
Additional notes
in is a Rcpp::LogicalVector::iterator for non-logical matrices, the result may not behave as expected.
For numeric_matrix instances, double-precision values in (-1, 1) are coerced to zero due to double-to-integer casting in C++.
This is not consistent with the behaviour in R for non-zero values, which are coerced to TRUE.
For integer_matrix instances, integer values are not coerced to {0, 1} when they are assigned to *in.
Thus, even though the interpretation is correct, the vector produced will not be equivalent to the result of an as.logical call.
As a general rule, it is unwise to use Rcpp::LogicalVector::iterators for anything other than logical_matrix access.character_matrix data, we do not return raw const char* pointers to the C-style string.
Rather, the Rcpp::String class is used as it provides a convenient wrapper around the underlying CHARSXP.
This ensures that the string is stored in R’s global cache and is suitably protected against garbage collection.The following matrix classes are natively supported by the API:
matrix, dgeMatrix, dgCMatrixmatrixmatrix, lgeMatrix, lgCMatrixmatrixThe API will also natively support DelayedMatrix objects using the above matrices as backends and containing only subsetting or transposition operations.
It is possible to natively support arbitrary user-supplied matrices, see here for more details and HDF5Array for an example.
For all other matrices, the API indirectly supports data access via a block processing mechanism.
This involves a call to R to realize a block of the matrix (containing the requested row or column) as a dense contiguous array.
A block is realized so that further requests to rows/columns within the same block do not involve a new call to R.
The size of the blocks can be controlled using methods in the DelayedArray package, see ?blockGrid for details.
Additional notes
yield method can be used to obtain the original Rcpp::RObject for input to RcppArmadillo or RcppEigen.
This functionality is generally limited to base matrices, though there is also limited support for sparse matrices in these libraries.For specific matrix representations, special methods are available that can improve the efficiency of column-level data access.
dgeMatrix instances are stored as dense arrays, so it is possible to access the columns without copying by returning an iterator to the start of each column.dgCMatrix, the column-sparse format allows us to access the non-zero values (and their row indices) directly for each column without copying.The const_column class provides a convenient wrapper to exploit these optimizations where possible.
#include "beachmat/utils/const_column.h"
// Need a get() as unique_ptr's are not copyable.
beachmat::const_column<beachmat::numeric_matrix> col_holder(dptr.get());The fill method will instruct the const_column object to obtain the relevant column,
taking advantage of no-copy methods if supported by the representation.
For other matrices, it simply calls get_col() to perform a copy to its internal storage.
col_holder.fill(
c /* size_t */,
first /* size_t */,
last /* size_t */
);The first and last arguments are optional and behave as previously described.
Additional notes
const_column instance should not exceed that of the numeric_matrix with which it was constructed.
This is because the former holds a pointer to the latter, which would no longer be valid upon destruction.
The const_column also keeps iterators to the underlying R-managed data, which could be invalidated upon numeric_matrix destruction in some contrived scenarios.An iterator to the values of the column is obtained with get_values:
Rcpp::NumericVector::iterator val=col_holder.get_values(); An iterator to the row index of each value is obtained with get_indices:
Rcpp::IntegerVector::iterator idx=col_holder.get_indices();The number of values pointed to by the iterator is obtained with get_n:
size_t n=col_holder.get_n(); Obviously, sparse matrices will not store any zeroes in the array of values pointed to by get_values(),
nor will the row indices for zeroes be present in the array pointed to by get_indices().
This may or may not require some custom code to take maximum advantage of sparsity:
if (col_holder.is_sparse()) {
// Do something fast with non-zero elements.
} else {
// Do something with all elements.
}Some applications require representation of all elements including zeroes, e.g., when the subsequent array needs to be accessed by row index.
We can ensure that we obtain an iterator to a dense array by constructing the const_column with the allow_sparsity argument turned off:
beachmat::const_column<beachmat::numeric_matrix> col_holder(
dptr.get(), false);Doing so will force const_column to use get_col() for accessing sparse matrices, instead of obtaining iterators to the raw structure.
However, no-copy optimizations for dense matrices will still be active.
For non-sparse matrices, calling get_indices() will cause an internal array to be populated with consecutive iterators.
One can share this array across many const_column instances by calling get_indices() prior to construction of copies:
beachmat::const_column<beachmat::numeric_matrix> col_holder(dptr.get());
col_holder.get_indices(); // Effectively 'static' indices.
// Make any number of copies without re-generating the indices.
auto holder_copy=col_holder;This can save some memory if many const_column objects are to be created.
The clone() method returns a unique pointer to a numeric_matrix instance of the same type as that pointed to by dptr.
auto dptr_copy = dptr->clone();This is occasionally useful, e.g., when row and column access is simultaneously required from the same matrix.
In such cases, row-specific settings in a single numeric_matrix instance (e.g., for HDF5 caching) would preclude efficient column extraction, and vice versa.
These problems are avoided by having two separate instances for row and column access.
Cloning also enables multi-threaded access to the same matrix data.
Ordinarily, the get* methods in beachmat are not thread safe.
Some methods use cached class members for greater efficiency, and simultaneous calls will cause race conditions.
It is the responsibility of the calling function to coordinate data access across threads.
To this end, the clone method can be called to generate a unique pointer to a new *_matrix instance, which can be used concurrently in another thread.
This is fairly cheap as the underlying matrix data are not copied.
An example of parallelized beachmat code using OpenMP might look like this:
#pragma omp parallel num_threads(nthreads)
{
beachmat::numeric_matrix* rptr=NULL;
std::unique_ptr<beachmat::numeric_matrix> uptr=nullptr;
if (omp_get_thread_num()==0) {
rptr=dptr.get();
} else {
uptr=dptr->clone();
rptr=uptr.get();
}
const size_t NC=rptr->get_ncol();
Rcpp::NumericVector output(rptr->get_nrow());
#pragma omp for schedule(static)
for (size_t col=0; col<NC; ++col) {
// Do parallel operation here.
rptr->get_col(col, output.begin());
}
}The start of the parallel region uses the existing dptr in the master thread and clones a new matrix in the other threads.
The parallelized for loop then uses rptr to avoid race conditions in cached variables.
Note that a static schedule may be faster than other schedule types, as several of the matrix implementations in beachmat are optimized for consecutive row/column access.
Additional notes