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primarykey
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17974990
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0
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0
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2013-07-31T15:48:10.527
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2013-08-15T18:02:33.967
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2013-08-15T18:02:33.967
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2628508
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2628508
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17925733
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2
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There is actually no need to dynamically load a shared library linking to culatools. I thought about this initially, but I did not get the .cpp file using Rcpp compiled. Anyway, the new .cpp file is: <pre><code>#include<Rcpp.h> #include<cula.h> using namespace Rcpp; using namespace std; RcppExport SEXP gpuQR_Rcpp(SEXP x_, SEXP n_rows_, SEXP n_cols_) { vector<float> x = as<vector<float> >(x_); int n_rows = as<int>(n_rows_); int n_cols = as<int>(n_cols_); vector<float> scale(n_cols); culaInitialize(); culaSgeqrf(n_rows, n_cols, &(x[0]), n_rows, &(scale[0])); culaShutdown(); 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]; } } } return wrap(x); } </code></pre> The .cpp file is compiled using: <pre><code>library(Rcpp) PKG_LIBS <- sprintf('-Wl,-rpath,/usr/local/cula/lib64 -L/usr/local/cula/lib64 -lcula_lapack %s $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)', Rcpp:::RcppLdFlags()) PKG_CPPFLAGS <- sprintf('-I/usr/local/cula/include %s', Rcpp:::RcppCxxFlags()) Sys.setenv(PKG_LIBS = PKG_LIBS , PKG_CPPFLAGS = PKG_CPPFLAGS) R <- file.path(R.home(component = 'bin'), 'R') file <- 'path/gpuQR_inc.cpp' cmd <- sprintf('%s CMD SHLIB %s', R, paste(file, collapse = ' ')) system(cmd) </code></pre> where I set the appropriate path to culatools. The whole thing does not run faster, but there is no need any longer to compile the shared library linking to culatools and dynamically loading it. I think that this is a nice alternative to the gputools R-package to extend R with C++ and perform linear algebra operations on the GPU. 
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1. PORcpp and CULA: segmentation fault
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