MAGMA and Rcpp for linear algebra in R

I was wondering if anybody has tried to use Rcpp and MAGMA to accelerate linear algebra operations in R by using the CPU and GPU? I tried culatools last month and it worked with Rcpp (link), but culatools is a commercial product that costs money to get access to all functions.


It was pretty straightforward to use Rcpp and MAGMA after having tinkered around with culatools. Here is the .cpp file:


using namespace Rcpp;

RcppExport SEXP gpuQR_magma(SEXP X_)
    // Input
    NumericMatrix X(X_);

    // Initialize magma and cublas

    // Declare variables 
    int info, lwork, n_rows = X.nrow(), n_cols = X.ncol(), min_mn = min(n_rows, n_cols);
    double tmp[1];
    NumericVector scale(min_mn);

    // Query workspace size
    magma_dgeqrf(n_rows, n_cols, &(X[0]), n_rows, &(scale[0]), &(work[0]), -1, &info); 
    lwork = work[0];
    NumericVector work(lwork);

    // Run QR decomposition
    magma_dgeqrf(n_rows, n_cols, &(X[0]), n_rows, &(scale[0]), &(work[0]), lwork, &info);

    // Scale factor result    
    for(int ii = 1; ii < n_rows; ii++)
        for(int jj = 0; jj < n_cols; jj++)
            if(ii > jj) { X[ii + jj * n_rows] *= scale[jj]; }

    // Shutdown magma and cublas    

    // Output  
    return wrap(X);

The file can be compiled from R into a shared library using:

PKG_LIBS <- sprintf('-Wl,-rpath,/usr/local/magma/lib -L/usr/local/magma/lib -lmagma /usr/local/magma/lib/libmagma.a -Wl,-rpath,/usr/local/cuda-5.5/lib64 %s', Rcpp:::RcppLdFlags()) 
PKG_CPPFLAGS <- sprintf('-DADD_ -DHAVE_CUBLAS -I/usr/local/magma/include -I/usr/local/cuda-5.5/include %s', Rcpp:::RcppCxxFlags())  
R <- file.path(R.home(component = 'bin'), 'R') 
file <- '/path/gpuQR_magma.cpp'
cmd <- sprintf('%s CMD SHLIB %s', R, paste(file, collapse = ' '))

The shared library can now be called in R. Comparing the results with with R's qr() gives:


n_row <- 3; n_col <- 3
A <- matrix(rnorm(n_row * n_col), n_row, n_col)


           [,1]       [,2]       [,3]
[1,]  0.5250957 -0.8666925  0.8594266
[2,] -0.2504899 -0.3878643 -0.1277838
[3,]  0.1502909  0.4742033 -0.8804247

.Call('gpuQR_magma', A)

           [,1]       [,2]       [,3]
[1,]  0.5250957 -0.8666925  0.8594266
[2,] -0.2504899 -0.3878643 -0.1277838
[3,]  0.1502909  0.4742033 -0.8804247

Below are the results from a benchmark using a NVIDIA GeForce GTX 675MX GPU with 960 CUDA cores and OpenBLAS:

n_row <- 3000; n_col <- 3000
A <- matrix(rnorm(n_row * n_col), n_row, n_col)
B <- A; dim(B) <- NULL

res <- benchmark(.Call('gpuQR_magma', A), .Call('gpuQR_cula', B, n_row, n_col), qr(A), columns = c('test', 'replications', 'elapsed', 'relative'), order = 'relative')

                                   test replications elapsed relative
2  .Call("gpuQR_cula", B, n_row, n_col)          100  18.704    1.000
1               .Call("gpuQR_magma", A)          100  70.461    3.767
3                                 qr(A)          100 985.411   52.685

Seems like as if MAGMA is a bit slower compared to culatools (in this example). However, MAGMA comes with out-of-memory implementations and that is something I value a lot given only 1Gb of GPU memory.

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