From alireza.s.mahani at gmail.com  Wed Jul 13 15:28:55 2011
From: alireza.s.mahani at gmail.com (asmahani)
Date: Wed, 13 Jul 2011 06:28:55 -0700 (PDT)
Subject: [Rd] Performance of .C and .Call functions vs. native R code
Message-ID: <1310563735586-3665017.post@n4.nabble.com>

Hello,

I am in the process of writing an R extension for parallelized MCMC, with
heavy use of compiled code (C++). I have been getting my feet wet by
implementing a simple matrix-vector multiplication function in C++ (which
calls a BLAS level 2 function dgemv), and comparing it to the '%*%' operator
in R (which apparently calls a BLAS level 3 function dgemm).

Interestingly, I cannot replicate the performance of the R native operator,
using either '.C' or '.Call'. The relative times are 17 (R), 30 (.C), and 26
(.Call). In other words, R native operator is 1.5x faster than my compiled
code. Can you explain to me why this is? Through testing I strongly suspect
that the BLAS function itself isn't what takes the bulk part of the time,
but perhaps data transfer and other overhead associated with the calls (.C
and .Call) are the main issues. Are there any ways to reach the performance
level of native R code in this case?

Thank you,
Alireza Mahani

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[Rd] Performance of .C and .Call functions vs. native R code
asmahani