# 1 Background

Ordinarily, direct support for an input matrix class would require the appropriate methods to be defined in beachmat at compile time. This is the case for the most widely used matrix classes but is somewhat restrictive for other community-contributed matrix representations. Fortunately, R provides a mechanism to link across shared libraries from different packages. This means that package developers who define a R matrix type can also define C++ methods for direct input support in beachmat-dependent code. By doing so, we can improve efficiency of access to these new classes by avoiding the need for block processing via R.

A functioning demonstration of this approach is available in the extensions test package. This vignette will provide an explanation of the code in extensions, and we suggest examining the source code at the same time:

system.file("extensions", package="beachmat")
## [1] "/tmp/RtmpS3CKSB/Rinst3ae91d9a18df/beachmat/extensions"

# 2 Setting up in R

Assume that we have already defined a new matrix-like S4 class (here, AaronMatrix). To notify the beachmat API that direct access support is available, we need to:

• define a method for the supportCppAccess() generic (from beachmat) for this class. This should return TRUE if direct support is available (obviously).
• define a method for the type() generic from the DelayedArray package. This should return the type of the matrix, i.e., integer, logical, numeric or character.

It is possible to only have direct support for particular data types of the given matrix representation. The example in extensions only directly supports integer and character AaronMatrix objects1 Because I was too lazy to add all of them. and will only return TRUE for such types.

# 3 Setting up in C++

We will use integer matrices for demonstration, though it is simple to generalize this to all types by replacing _integer with, e.g., _character2 Some understanding of C++ templates will greatly simplify the definition of the same methods for different types.. First, we define a create() function that takes a SEXP object and returns a void pointer. This should presumably point to some C++ class that can contain intermediate data structures for efficient access.

void * ptr = create_integer(in /* SEXP */);

We define a clone() function that performs a deep copy of the aforementioned pointer.

void * ptr_copy = clone_integer(ptr /* void* */);

We define a destroy() function the frees the memory pointed to by ptr.

destroy_integer(ptr /* void* */);

We define a get_dim() function that records the number of rows and columns in the object pointed to by ptr. Note the references on the size_t& arguments.

get_dim_integer(
ptr, /* void* */
nrow, /* size_t& */
ncol /* size_t& */
);

# 4 Defining getter methods

## 4.1 For all types

In general, the getter methods follow the same structure as that described for the input API. We expect a load() method to obtain a specified entry of the matrix:

int val = load_integer(
ptr, /* void* */
r, /* size_t */
c /* size_t */
);

The returned val should reflect the matrix type. For example, val should be a Rcpp::String for character matrices, a double for numeric matrices, and an int for logical matrices.

Developers can assume that r and c are valid, i.e., within [0, nrow) and [0, ncol) respectively. These checks are performed by beachmat and do not have to be repeated within developer-defined functions3 Obviously, the dimensions of the matrix pointed to by ptr should not change!.

## 4.2 For non-numeric types

Here, we will use character matrices4 Character matrices tend to require some special attention, as character arrays need to be coerced to Rcpp::String objects to be returned in in. as an example. We expect a load_col() method to obtain a column of the matrix:

load_col_character(
ptr, /* void* */
c, /* size_t */
in, /* Rcpp::StringVector::iterator */
first, /* size_t */
last /* size_t */
);

… and a load_row() method to obtain a row of the matrix:

load_row_character(
ptr, /* void* */
r, /* size_t */
in, /* Rcpp::StringVector::iterator */
first, /* size_t */
last /* size_t */
);

We expect a load_cols() method to obtain multiple columns:

load_cols_character(
ptr, /* void* */
indices, /* Rcpp::IntegerVector::iterator */
n, /* size_t */
in, /* Rcpp::StringVector::iterator */
first, /* size_t */
last /* size_t */
);

… and a load_rows() method to obtain multiple rows:

load_cols_character(
ptr, /* void* */
indices, /* Rcpp::IntegerVector::iterator */
n, /* size_t */
in, /* Rcpp::StringVector::iterator */
first, /* size_t */
last /* size_t */
);

In all cases, first and last can be assumed to be valid, i.e., first <= last and both in [0, nrow) or [0, ncol) (for column and row access, respectively). Indices in indices can also be assumed to be valid, i.e., within matrix dimensions and strictly increasing.

## 4.3 Numeric types

For integer, logical or numeric matrices, we need to account for type conversions. This is done by defining the following functions (using integer matrices as an example):

• load_col2int_integer
• load_col2dbl_integer
• load_row2int_integer
• load_row2dbl_integer
• load_cols2int_integer
• load_cols2dbl_integer
• load_rows2int_integer
• load_rows2dbl_integer

Taking the single-column getter as an example:

load_col2int_character(
ptr, /* void* */
c, /* size_t */
in, /* Rcpp::IntegerVector::iterator */
first, /* size_t */
last /* size_t */
);

ptr, /* void* */
c, /* size_t */
in, /* Rcpp::NumericVector::iterator */
first, /* size_t */
last /* size_t */
);

We explicitly define conversions here as the cross-library linking framework does not support templating of in. If we only defined a load_col() function of the same type, we would need to perform two copies: once to copy to an integer vector, and then another to copy to the output double-precision vector.

# 5 Defining special getters

## 5.1 Constant column access

We define a load_const_col() function to obtain an iterator to a contiguous stretch of memory defining a column of the matrix. Again, ptr is a pointer to the location in memory containing the matrix object. All other arguments are as described in the previous workflow.

Rcpp::IntegerVector::iterator out = load_const_col_integer(
ptr, /* void* */
c, /* size_t */
in, /* Rcpp::IntegerVector::iterator */
first, /* size_t */
last /* size_t */
);

This function should only be special for representations where entire columns are stored contiguously. All other representations should simply copy data into in. It is unwise to try to be too smart, as the returned iterator out must be valid throughout the lifetime of the matrix. This means that, if all columns were accessed, the entire matrix would need to be stored in memory to ensure that all iterators were valid. Such a strategy means that any matrix representation will effectively become a dense array.

Obviously, out and in should reflect the matrix type. For example, for load_const_col_character(), both of them should be Rcpp::StringVector::iterator objects.

## 5.2 Indexed column access

We define a load_const_col_indexed() function to obtain iterators to “non-zero” elements of the matrix5 See the previous workflow to clarify the definition of non-zero.. Again, ptr is a pointer to the location in memory containing the matrix object. The number of indexed elements should be returned in n.

size_t n = load_const_col_integer(
ptr, /* void* */
c, /* size_t */
index, /* Rcpp::IntegerVector::iterator& */
values, /* Rcpp::IntegerVector::iterator& */
first, /* size_t */
last /* size_t */
);

Note that both index and values are references to iterator objects. They should be modified to point to internal data structures in ptr. The modified values will then be returned as part of the output of get_const_col_indexed() in the input API.

This function should only be special for representations where non-zero values and their indices are stored by column (i.e., variants of column-sparse compressed matrices). All other representations should simply copy data into values, and set index to a sequence of integers from [first, last). It is possible to streamline the setting of index by creating an increasing sequence once and storing it in ptr - see the example in extensions for more details.

Obviously, values should reflect the matrix type. For example, for load_const_col_character(), it should be a Rcpp::StringVector::iterator& reference.

# 6 Ensuring discoverability

We use the R_RegisterCCallable() function from the R API to register the above functions (see here for an explanation). This ensures that they can be found by beachmat when an AaronMatrix instance is encountered. Note that the functions must be defined with C-style linkage in order for this procedure to work properly, hence the use of extern "C".

Needless to say, the NAMESPACE should contain an appropriate useDynLib command. This means that shared library will be loaded along with the package, allowing beachmat to access the registered routines within.