Dirk E. has properly focussed the discussion on measurement rather than opinion. I'll add
the issue of the human time taken to convert, and more importantly debug, interfaced code.
That too could be measured, but we rarely see human hours to code/debug/test reported.
Moreover, I'll mention the
Don't underestimate the importance of the choice of the algorithm you
use. That often makes a huge difference. Also, vectorization is key
in R, and when you use that you're really up there among the top
performing languages. Here is an example from the official R wiki
illustrating my points:
On Fri, Sep 10, 2010 at 10:23 AM, Henrik Bengtsson h...@stat.berkeley.edu
wrote:
Don't underestimate the importance of the choice of the algorithm you
use. That often makes a huge difference. Also, vectorization is key
in R, and when you use that you're really up there among the top
-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1
On 09/09/10 12:26, Christofer Bogaso wrote:
Dear all, R offers integration mechanism with different programming
languages like C, C++, Fortran, .NET etc. Therefore I am curious on,
for heavy numerical computation which language is the fastest? Is
For the compiled languages, it depends heavily on the compiler. This
sort of comparison is rendered moot by the huge variety of compiler and
hardware specific optimizations. My suggestion is to use C, or possibly
C++ in conjunction with Rcpp, as these are most compatible with R. Also,
C and C++
On 9 September 2010 at 13:26, Rainer M Krug wrote:
| -BEGIN PGP SIGNED MESSAGE-
| Hash: SHA1
|
| On 09/09/10 12:26, Christofer Bogaso wrote:
| Dear all, R offers integration mechanism with different programming
| languages like C, C++, Fortran, .NET etc. Therefore I am curious on,
|
6 matches
Mail list logo
