The RcppParallel package includes
high level functions for doing parallel programming with Rcpp. For example,
the parallelFor
function can be used to convert the work of a standard
serial “for” loop into a parallel one.
This article describes using RcppParallel to compute pairwise distances for
each row in an input data matrix and return an n x n lowertriangular
distance matrix which can be used with clustering tools from within R, e.g.,
hclust.
JensenShannon Distance
In this example, we compute the JensenShannon
distance
(JSD); a metric not a part of base R. Calculating distance matrices is a
common practice in clustering applications (unsupervised learning). Certain
clustering methods, such as partitioning around medoids (PAM) and
hierarchical clustering, operate directly on this matrix.
A distance matrix stores the n*(n1)/2 pairwise distances/similarities
between observations in an n x p matrix where n correspond to the independent
observational units and p represent the covariates measured on each
individual. As a result we are typically limited by the size of n as the
algorithm scales quadratically in both time and space in n.
Implementation in R
As a baseline we’ll start with the implementation of JensonShannon distance
in plain R:
js_distance < function(mat) {
kld = function(p,q) sum(ifelse(p == 0  q == 0, 0, log(p/q)*p))
res = matrix(0, nrow(mat), nrow(mat))
for (i in 1:(nrow(mat)  1)) {
for (j in (i+1):nrow(mat)) {
m = (mat[i,] + mat[j,])/2
d1 = kld(mat[i,], m)
d2 = kld(mat[j,], m)
res[j,i] = sqrt(.5*(d1 + d2))
}
}
res
}
Implementation using Rcpp
Here is a reimplementation of js_distance
using Rcpp. Note that this
doesn’t yet take advantage of parallel processing, but still yields an
approximately 50x speedup over the original R version on a 2.6GHz Haswell
MacBook Pro.
Abstractly, a Distance function will take two vectors in R^{J} and
return a value in R^{+}. In this implementation, we don’t support
arbitrary distance metrics, i.e., the JSD code computes the values from
within the parallel kernel.
Our distance function kl_divergence
is defined below and takes three
parameters: iterators to the beginning and end of vector 1 and an iterator to
the beginning of vector 2 (the end position of vector2 is implied by the end
position of vector1).
#include
using namespace Rcpp;
#include
#include
// generic function for kl_divergence
template <typename InputIterator1, typename InputIterator2>
inline double kl_divergence(InputIterator1 begin1, InputIterator1 end1,
InputIterator2 begin2) {
// value to return
double rval = 0;
// set iterators to beginning of ranges
InputIterator1 it1 = begin1;
InputIterator2 it2 = begin2;
// for each input item
while (it1 != end1) {
// take the value and increment the iterator
double d1 = *it1++;
double d2 = *it2++;
// accumulate if appropirate
if (d1 > 0 && d2 > 0)
rval += std::log(d1 / d2) * d1;
}
return rval;
}
With the kl_distance
function defined we can now iteratively apply it
to the rows of the input matrix to generate the distance matrix:
// helper function for taking the average of two numbers
inline double average(double val1, double val2) {
return (val1 + val2) / 2;
}
// [[Rcpp::export]]
NumericMatrix rcpp_js_distance(NumericMatrix mat) {
// allocate the matrix we will return
NumericMatrix rmat(mat.nrow(), mat.nrow());
for (int i = 0; i < rmat.nrow(); i++) {
for (int j = 0; j < i; j++) {
// rows we will operate on
NumericMatrix::Row row1 = mat.row(i);
NumericMatrix::Row row2 = mat.row(j);
// compute the average using std::tranform from the STL
std::vector<double> avg(row1.size());
std::transform(row1.begin(), row1.end(), // input range 1
row2.begin(), // input range 2
avg.begin(), // output range
average); // function to apply
// calculate divergences
double d1 = kl_divergence(row1.begin(), row1.end(), avg.begin());
double d2 = kl_divergence(row2.begin(), row2.end(), avg.begin());
// write to output matrix
rmat(i,j) = std::sqrt(.5 * (d1 + d2));
}
}
return rmat;
}
Parallel Version using RcppParallel
Adapting the serial version to run in parallel is straightforward. A few
notes about the implementation:

To implement a parallel version we need to create a function
object that can process
discrete chunks of work (i.e. ranges of input). 
Since the parallel version will be called from background threads, we can’t
use R and Rcpp APIs directly. Rather, we use the threadsafeRMatrix
accessor class provided by RcppParallel to read and write to directly the
underlying matrix memory. 
Other than organzing the code as a function object and using
RMatrix
, the
parallel code is almost identical to the serial code. The main difference is
that the outer loop starts with thebegin
index passed to the worker
function rather than 0.
Parallelizing in this case has a big payoff: we observe performance of about
5.5x the serial version on a 2.6GHz Haswell MacBook Pro with 4 cores (8 with
hyperthreading). Here is the definition of the JsDistance
function object:
// [[Rcpp::depends(RcppParallel)]]
#include
using namespace RcppParallel;
struct JsDistance : public Worker {
// input matrix to read from
const RMatrix<double> mat;
// output matrix to write to
RMatrix<double> rmat;
// initialize from Rcpp input and output matrixes (the RMatrix class
// can be automatically converted to from the Rcpp matrix type)
JsDistance(const NumericMatrix mat, NumericMatrix rmat)
: mat(mat), rmat(rmat) {}
// function call operator that work for the specified range (begin/end)
void operator()(std::size_t begin, std::size_t end) {
for (std::size_t i = begin; i < end; i++) {
for (std::size_t j = 0; j < i; j++) {
// rows we will operate on
RMatrix<double>::Row row1 = mat.row(i);
RMatrix<double>::Row row2 = mat.row(j);
// compute the average using std::tranform from the STL
std::vector<double> avg(row1.length());
std::transform(row1.begin(), row1.end(), // input range 1
row2.begin(), // input range 2
avg.begin(), // output range
average); // function to apply
// calculate divergences
double d1 = kl_divergence(row1.begin(), row1.end(), avg.begin());
double d2 = kl_divergence(row2.begin(), row2.end(), avg.begin());
// write to output matrix
rmat(i,j) = sqrt(.5 * (d1 + d2));
}
}
}
};
Now that we have the JsDistance
function object we can pass it to
parallelFor
, specifying an iteration range based on the number of rows in
the input matrix:
// [[Rcpp::export]]
NumericMatrix rcpp_parallel_js_distance(NumericMatrix mat) {
// allocate the matrix we will return
NumericMatrix rmat(mat.nrow(), mat.nrow());
// create the worker
JsDistance jsDistance(mat, rmat);
// call it with parallelFor
parallelFor(0, mat.nrow(), jsDistance);
return rmat;
}
Benchmarks
We now compare the performance of the three different implementations: pure
R, serial Rcpp, and parallel Rcpp:
# create a matrix
n = 1000
m = matrix(runif(n*10), ncol = 10)
m = m/rowSums(m)
# ensure that serial and parallel versions give the same result
r_res < js_distance(m)
rcpp_res < rcpp_js_distance(m)
rcpp_parallel_res < rcpp_parallel_js_distance(m)
stopifnot(all(rcpp_res == rcpp_parallel_res))
stopifnot(all(rcpp_parallel_res  r_res < 1e10)) ## precision differences
# compare performance
library(rbenchmark)
res < benchmark(js_distance(m),
rcpp_js_distance(m),
rcpp_parallel_js_distance(m),
replications = 3,
order="relative")
res[,1:4]
test replications elapsed relative 3 rcpp_parallel_js_distance(m) 3 0.110 1.000 2 rcpp_js_distance(m) 3 0.618 5.618 1 js_distance(m) 3 35.560 323.273
The serial Rcpp version yields a more than 50x speedup over straight R code.
The parallel Rcpp version provides another 5.5x speedup, amounting to a total
gain of over 300x compared to the original R version.
Note that performance gains will typically be 3050% less on Windows systems
as a result of less sophisticated thread scheduling (RcppParallel does not
currently use TBB on Windows
whereas it does on the Mac and Linux).
You can learn more about using RcppParallel at
https://github.com/RcppCore/RcppParallel.
Rbloggers.com offers daily email updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...