Multiplying complex matrices in R using C++ - c++

Question

Suppose that A is a complex matrix. I am interested in computing the product A%*%Conj(t(A)) in R efficiently. As far as I understand, using C++ would speed up things significantly, so that is what I am trying to do.
I have the following code for real matrices that I can use in R.
library(Rcpp);
library(inline);
library(RcppEigen);
crossprodCpp <- '
using Eigen::Map;
using Eigen::MatrixXd;
using Eigen::Lower;
const Map<MatrixXd> A(as<Map<MatrixXd> >(AA));
const int m(A.rows());
MatrixXd AAt(MatrixXd(m, m).setZero().selfadjointView<Lower>().rankUpdate(A));
return wrap(AAt);
'
fcprd <- cxxfunction(signature(AA = "matrix"), crossprodCpp, "RcppEigen")
A<-matrix(rnorm(100^2),100)
all.equal(fcprd(A),tcrossprod(A))
fcprd(A) runs much faster on my laptop than tcrossprod(A). This is what I get for A<-matrix(rnorm(1000^2),1000):
microbenchmark::microbenchmark('tcrossprod(A)'=tcrossprod(A),'A%*%t(A)'=A%*%t(A),fcprd=fcprd(A))
Unit: milliseconds
expr min lq mean median uq max neval
tcrossprod(A) 428.06452 435.9700 468.9323 448.8168 504.2628 618.7681 100
A%*%t(A) 722.24053 736.6197 775.4814 767.7668 809.8356 903.8592 100
fcprd 95.04678 100.0733 111.5021 103.6616 107.2551 197.4479 100
However, this code only works for matrices with double precision entries. How could I modify this code so that it works for complex matrices?
I have a very limited knowledge of programming, but I am trying to learn.
Any help is much appreciated!

Answer 1

The Eigen library supports also complex entries via Eigen::MatrixXcd. So in principle it should work if you replace MatrixXd with MatrixXcd. However, this does not compile probably because there is no as-function for complex matrices using Map (c.f. https://github.com/RcppCore/RcppEigen/blob/master/inst/unitTests/runit.RcppEigen.R#L205). The as-function are needed to convert between R data types and C++/Eigen data types (c.f. http://dirk.eddelbuettel.com/code/rcpp/Rcpp-extending.pdf). If you do not use Map, then you can use this:
crossprodCpp <- '
using Eigen::MatrixXcd;
using Eigen::Lower;
const MatrixXcd A(as<MatrixXcd>(AA));
const int m(A.rows());
MatrixXcd AAt(MatrixXcd(m, m).setZero().selfadjointView<Lower>().rankUpdate(A));
return wrap(AAt);
'
fcprd <- inline::cxxfunction(signature(AA = "matrix"), crossprodCpp, "RcppEigen")
N <- 100
A <- matrix(complex(real = rnorm(N), imaginary = rnorm(N)), N)
all.equal(fcprd(A), A %*% Conj(t(A)))
However, this is slower than the base R version in my tests:
N <- 1000
A <- matrix(complex(real = rnorm(N * N), imaginary = rnorm(N * N)), N)
all.equal(fcprd(A), A %*% Conj(t(A)))
#> [1] TRUE
microbenchmark::microbenchmark(base = A %*% Conj(t(A)), eigen = fcprd(A))
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> base 111.6512 124.4490 145.7583 140.9199 160.3420 241.8986 100
#> eigen 453.6702 501.5419 535.0192 537.2925 564.8746 628.4999 100
Note that matrix multiplication in R is done via BLAS. However, the default BLAS implementation used by R is not very fast. One way to improve R's performance is to use an optimized BLAS library, c.f. https://csgillespie.github.io/efficientR/set-up.html#blas-and-alternative-r-interpreters.
Alternatively you can use the BLAS function zherk if you have a full BLAS available. Very rough:
dyn.load("/usr/lib/libblas.so")
zherk <- function(a, uplo = 'u', trans = 'n') {
n <- nrow(a)
k <- ncol(a)
c <- matrix(complex(real = 0, imaginary = 0), nrow = n, ncol = n)
z <- .Fortran("zherk",
uplo = as.character(uplo),
trans = as.character(trans),
n = as.integer(n),
k = as.integer(k),
alpha = as.double(1),
a = as.complex(a),
lda = as.integer(n),
beta = as.double(0),
c = as.complex(c),
ldc = as.integer(n))
matrix(z$c, nrow = n, ncol = n)
}
N <- 2
A <- matrix(complex(real = rnorm(N * N), imaginary = rnorm(N * N)), N)
zherk(A, uplo = "l") - A %*% Conj(t(A))
Note that this fills only the upper (or lower) triangular part but is quite fast:
microbenchmark::microbenchmark(base = A %*% Conj(t(A)), blas = zherk(A))
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> base 112.5588 117.12531 146.10026 138.37565 167.6811 282.3564 100
#> blas 66.9541 70.12438 91.44617 82.74522 108.4979 188.3728 100

Answer 2

Here is a way to bind an Eigen::Map<Eigen::MatrixXcd> object in Rcpp. The solution works in a R package setup, but I'm not sure about an easy way to put it together using the inline library.
First, you need to provide the following specialization in your inst/include/mylib.h such that this header get included in the RcppExports.cpp:
#include <complex>
#include <Eigen/Core>
#include <Eigen/Dense>
#include <Rcpp.h>
namespace Rcpp {
namespace traits {
template<>
class Exporter<Eigen::Map<Eigen::Matrix<std::complex<double>, Eigen::Dynamic, Eigen::Dynamic> > > {
using OUT = typename Eigen::Map<Eigen::Matrix<std::complex<double>, Eigen::Dynamic, Eigen::Dynamic> >;
const static int RTYPE = ::Rcpp::traits::r_sexptype_traits<std::complex<double>>::rtype;
Rcpp::Vector<RTYPE> vec;
int d_ncol, d_nrow;
public:
Exporter(SEXP x)
: vec(x), d_ncol(1)
, d_nrow(Rf_xlength(x)) {
if (TYPEOF(x) != RTYPE)
throw std::invalid_argument("Wrong R type for mapped matrix");
if (::Rf_isMatrix(x)) {
int* dims = INTEGER(::Rf_getAttrib(x, R_DimSymbol));
d_nrow = dims[0];
d_ncol = dims[1];
}
}
OUT get() { return OUT(reinterpret_cast<std::complex<double>*>(vec.begin()), d_nrow, d_ncol); }
};
}}
The only difference with the unspecialized Exporter available in RcppEigenWrap.h being the reinterpret_cast on the last line. Both std::complex and Rcomplex having C99 complex compatible types, they are supposed to have identical memory layouts regardless of the implementation.
Wrapping it up, you can now create your function as:
// [[Rcpp::export]]
Eigen::MatrixXd selfadj_mult(const Eigen::Map<Eigen::MatrixXcd>& mat) {
Eigen::MatrixXd result = (mat * mat.adjoint()).real();
return result;
}
and then invoke the function in R as:
library(mylib)
library(microbenchmark)
N <- 1000
A <- matrix(complex(real = rnorm(N * N), imaginary = rnorm(N * N)), N)
microbenchmark::microbenchmark(
base = A %*% Conj(t(A))
, eigen = mylib::selfadj_mult(A)
, times = 100L
)
the code is compiled on centos7/gcc83 with -O3 -DNDEBUG -flto -march=generic. R has been build from source with the exact same compiler/flags (using the default BLAS binding). Results are:
Unit: seconds
expr min lq mean median uq max neval
base 2.9030320 2.9045865 2.9097162 2.9053835 2.9093232 2.9614318 100
eigen 1.1978697 1.2004888 1.2134219 1.2031046 1.2057647 1.3035751 100