1 Overview of the output API

This document describes the use of the beachmat API for storing 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"

Three types of output matrices are supported - simple matrix, *gCMatrix and HDF5Matrix objects. For example, a simple numeric output matrix with nrow rows and ncol columns is created by:

// returns a std::unique_ptr<numeric_output> object
auto odptr=beachmat::create_numeric_output(
    nrow, /* size_t */ 
    ncol, /* size_t */
    oparam /* beachmat::output_param */
);

The beachmat::output_param class specifies the output matrix representation. It is simply constructed by passing the class and package names:

beachmat::output_param simple_param; 
beachmat::output_param simple_param2("matrix", "base"); /* default */
beachmat::output_param sparse_param("dgCMatrix", "Matrix"); 

Other class/package combinations can be handled by external linkage if available. Otherwise, the output_param constructor will default to an ordinary matrix.

2 Dynamic choice of output type

Another option is to allow the function to dynamically choose the output type to match that of an existing matrix. This is useful for automatically choosing an output format that reflects the choice of input format. For example, if data are supplied to a function in a simple matrix, it would be reasonable to expect that the output is similarly small enough to be stored as a simple matrix. On the other hand, if the input is file-backed, it suggests that the output may also be very large and thus require file-backed storage.

Dynamic choice of output type is performed by using the Rcpp::Robject object containing the input matrix to initialize the output_param object. If we have a matrix object dmat, the output type can be matched to the input type with:

beachmat::output_param oparam(dmat /* Rcpp::RObject */);
auto odptr=beachmat::create_numeric_output(nrow, ncol, oparam);

A similar process can be used for a pointer dptr to an existing *_matrix instance:

beachmat::output_param oparam(dptr->get_class(), dptr->get_package());

3 Methods to store data

odptr->set_col(
    c, /* size_t */ 
    out /* Rcpp::Vector::iterator */
);

odptr->set_col(
    c, /* size_t */ 
    out, /* Rcpp::Vector::iterator */
    first, /* size_t */ 
    last /* size_t */
);

odptr->set_col_indexed(
    c, /* size_t */ 
    N, /* size_t */
    idx, /* Rcpp::IntegerVector::iterator */
    valm /* Rcpp::Vector::iterator */
);

odptr->set_row(
    r, /* size_t */
    out /* Rcpp::Vector::iterator */
);

odptr->set_row(
    r, /* size_t */ 
    out, /* Rcpp::Vector::iterator */
    first, /* size_t */
    last /* size_t */
);

odptr->set_row_indexed(
    r, /* size_t */ 
    N,  /* size_t */
    idx, /* Rcpp::IntegerVector::iterator */
    val, /* Rcpp::Vector::iterator */
);

odptr->set(
    r, /* size_t */ 
    c, /* size_t */
    Y /* double */
)

4 Returning a matrix object to R

The yield() method returns a Rcpp::RObject object containing a matrix to pass to R.

Rcpp::RObject out = odptr->yield();

This is commonly used at the end of the function to return a matrix to R:

return dptr->yield();

Note that this operation may involve an R-level memory allocation, which may subsequently trigger garbage collection. This is usually not a concern as Rcpp is excellent at protecting against unintended collection of objects. However, one exception is that of random number generation, where the destruction of the Rcpp::RNGScope may trigger a collection of unprotected SEXPs. This will almost always be the case when using yield() naively, as the construction of the matrix SEXP is done at the end of the function:

// Possible segfault:
extern "C" SEXP dummy1 () {
    auto odptr=beachmat::create_numeric_output(nrow, ncol, 
        beachmat::output_param());
    Rcpp::RNGScope rng;
    // Do something with random numbers and store in odptr.
    return odptr->yield();
}

One solution is to restrict the scope of the Rcpp::RNGScope. This ensures that there are no unprotected SEXP objects upon destruction of the RNGScope, as yield() has not yet been called.

extern "C" SEXP dummy2 () {
    auto odptr=beachmat::create_numeric_output(nrow, ncol, 
        beachmat::output_param());
    {
        Rcpp::RNGScope rng;
        // Do something with random numbers and store in odptr.
    }
    return odptr->yield();
}

5 Methods to read data

A subset of the access methods are also implemented for *_output objects:

• get_nrow() and get_ncol()
• get_row(), get_col() and get()
• get_class(), get_package() and clone().

These methods behave as described previously for *_matrix objects. They may be useful in situations where data are stored in an intermediate matrix and need to be queried before the matrix is fully filled.

In most applications, though, it is possible to fully fill the output matrix, call yield() and then create a numeric_matrix from the resulting Rcpp::RObject. This is often faster because certain optimizations become possible when beachmat knows that the supplied matrix is read-only (for example, get_const_col() and get_const_col_indexed()).

6 Other matrix types

Logical, integer and character output matrices are supported by changing the types in the creator function (and its variants):

// returns a std::unique_ptr<integer_output> 
auto oimat=beachmat::create_integer_output(nrow, ncol, beachmat::output_param());

// returns a std::unique_ptr<logical_output> 
auto olmat=beachmat::create_logical_output(nrow, ncol, beachmat::output_param());

// returns a std::unique_ptr<character_output> 
auto ocmat=beachmat::create_character_output(nrow, ncol, beachmat::output_param());

For integer, logical and numeric matrices, out can be an iterator for any Rcpp::NumericVector, Rcpp::IntegerVector or Rcpp::LogicalVector objects. For integer and logical matrices, Y should be an integer. For character matrices, out should be of type Rcpp::StringVector::iterator and Y should be a Rcpp::String object.

Additional notes

• Similar to the issue discussed previously, it is probably unwise to use anything but a Rcpp::LogicalVector::iterator as out when storing data in a logical_output. This is because type conversion at the C++ level will not give the same results as conversion at the R level.

7 Handling parallelization

The API is not thread-safe, due to (i) the use of cached class members and (ii) the potential for race conditions when writing to the same location on disk/memory. The first issue can be solved by using clone() to create *_output copies for use in each thread33 Excepting HDF5 matrices, see comments here.. However, each copy may still read from and write to the same disk/memory location. Furthermore, even if each copy writes to different rows or columns, they are not guaranteed to affect different parts of memory. (Storage of rows of a sparse matrix, for example, is dependent on the nature of previous rows.) It is thus the responsibility of the calling function to ensure that access is locked and unlocked appropriately across multiple threads, e.g., via #pragma omp critical.