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17936995
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2013-07-30T01:38:07.593
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The problem is solved by deleting <pre><code>dlclose(lib_handle); </code></pre> from .cpp file. This yields the following: <pre><code>#include <Rcpp.h> #include <dlfcn.h> using namespace Rcpp; using namespace std; typedef void (*func)(int*, int*, float*, int*, float*); 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); void* lib_handle = dlopen("path/gpuQR.so", RTLD_LAZY); if (!lib_handle) { Rcout << dlerror() << endl; } else { func gpuQR = (func) dlsym(lib_handle, "gpuQR"); gpuQR(&n_rows, &n_cols, &(x[0]), &n_rows, &(scale[0])); } 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 can be compiled in R using: <pre><code>library(Rcpp) PKG_LIBS <- sprintf('%s $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)', Rcpp:::RcppLdFlags()) PKG_CPPFLAGS <- sprintf('%s', Rcpp:::RcppCxxFlags()) Sys.setenv(PKG_LIBS = PKG_LIBS , PKG_CPPFLAGS = PKG_CPPFLAGS) R <- file.path(R.home(component = 'bin'), 'R') file <- 'path/gpuQR_Rcpp.cpp' cmd <- sprintf('%s CMD SHLIB %s', R, paste(file, collapse = ' ')) system(cmd) </code></pre> The actual .c file linking to culatools is: <pre><code>#include<cula.h> void gpuQR(const int *m, const int *n, float *a, const int *lda, float *tau) { culaInitialize(); culaSgeqrf(m[0], n[0], a, lda[0], tau); culaShutdown(); } </code></pre> It can be compiled using: <pre><code>gcc -c -I/usr/local/cula/include gpuQR.c gcc -shared -Wl,-rpath,/usr/local/cula/lib64 -L/usr/local/cula/lib64 -lcula_lapack -o gpuQR.so gpuQR.o </code></pre> The QR decomposition can then be performed in R using: <pre><code>dyn.load('path/gpuQR_Rcpp.so') set.seed(100) n_row <- 3 n_col <- 3 A <- matrix(rnorm(n_row * n_col), n_row, n_col) res <- .Call('gpuQR_Rcpp', c(A), n_row, n_col) matrix(res, n_row, n_col) [,1] [,2] [,3] [1,] 0.5250958 -0.8666927 0.8594266 [2,] -0.2504899 -0.3878644 -0.1277837 [3,] 0.1502908 0.4742033 -0.8804248 qr(A)$qr [,1] [,2] [,3] [1,] 0.5250957 -0.8666925 0.8594266 [2,] -0.2504899 -0.3878643 -0.1277838 [3,] 0.1502909 0.4742033 -0.8804247 </code></pre> Here are the results from a benchmark using a NVIDIA GeForce 9400M GPU with 16 CUDA cores: <pre><code>n_row <- 1000; n_col <- 1000 A <- matrix(rnorm(n_row * n_col), n_row, n_col) B <- A; dim(B) <- NULL res <- benchmark(.Call('gpuQR_Rcpp', B, n_row, n_col), qr(A), columns = c('test', 'replications', 'elapsed', 'relative'), order = 'relative') test replications elapsed relative 1 .Call("gpuQR_Rcpp", B, n_row, n_col) 100 38.037 1.000 2 qr(A) 100 152.575 4.011 </code></pre>
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