Statistical computing in C++ and R

The Need for Speed Part 2: C++ vs. Fortran vs. C
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Constance Garnett. Quinn University of Michigan , and Andrew D.

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Martin University of Michigan. It includes a suite of matrix manipulation functions, a suite of pseudo-random number generators, and a suite of numerical optimization routines.

We provide ease of use through three primary mechanisms: 1 operator and function over-loading, 2 numerous pre-fabricated utility functions, and 3 clear documentation and example programs. Additionally, Scythe is quite flexible and entirely extensible because the source code is available to all users under the GNU General Public License. We released Scythe 1.

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This release represents a major overhaul of the library since the last public release 0. Scythe now provides both column- and row-major matrices, multiple views of the same matrix, submatrix assignment, and a variety of other new functionality. R file is straightforward, especially after the previous post. The necessary evil is the Makevars in which I violated my own warning about having OpenMP called by two languages in the same package.

The entire package, which is not CRAN-worthy, can be found on github. There are sixteen implementations of the LLC function between the two packages.

(R-Studio) Tools required to build C/C++ Code for R

Timing was done using the microbenchmark package. The timing code can be found as Timings. In that script, the last commented-out call will create a nice table of all the timings. For vectors up to around length , the Fortran serial loop, direct, and elemental versions are approximately the same.

Introducing C/C++ for Statistical Computing and Integration with R (1/3)

Between and almost K, the Fortran serial loop is the fastest. Shortly before K, the overhead of the parallelization is overtaken by the speed of using multiple cores. The parallelization overhead is significant enough that even base R outperforms the compiled code up to almost 10K elements. Lastly, the. Select the graph below for a larger version. In closing, significant speed can be achieved in R by using compiled code. For this function, Fortran outperformed C and Rcpp, but that is not always the case. Hopefully, after these two posts, it will be easier for the interested reader to experiment, especially when needing to squeeze out every drop of speed.

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Below are a series of box and ridge plots comparing the distribution of the results across language and method. There are no ridge plots for lengths less than as ggridges returned an error for those. All graphs can be selected for a larger view. These timings were performed on an iK overclocked to 5. Fortran vs. C appeared first on Strange Attractors. To leave a comment for the author, please follow the link and comment on their blog: R — Strange Attractors.

Using C/C++ in R: R CMD SHLIB & Rcpp

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There are many ways to follow us - By e-mail: On Facebook: If you are an R blogger yourself you are invited to add your own R content feed to this site Non-English R bloggers should add themselves- here. Advanced R users from all professional groups. CHF The R system for statistical computing is based on a dynamic language called S.

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