Le 06/11/2013 18:35, Xavier Robin a écrit :
Hi,
I have a pure-R code that spends most of the time performing vector and
matrix operations, as shown by the summary of Rprof:
self.time self.pct total.time total.pct
"%*%" 903.24 77.67 903.24 77.67
"t.default" 76.26 6.56 76.26 6.56
"-" 36.60 3.15 36.60 3.15
"+" 24.44 2.10 24.44 2.10
"/" 24.22 2.08 24.22 2.08
"exp" 20.26 1.74 20.26 1.74
"predict.myClass" 17.68 1.52 503.82 43.32
"*" 11.90 1.02 11.90 1.02
"t" 9.38 0.81 811.94 69.82
"update.batch" 8.04 0.69 654.68 56.30
...
So mostly matrix %*% matrix multiplications, transpositions, vector +-/*
matrix operations and exponentiations, representing >95% of the
computation time.
I have very few loops and if/else blocks.
I want to speed up this code, and I am considering reimplementing it (or
part of it) with RcppEigen or RcppArmadillo.
However, I read that both Eigen and Amarillo use the underlying BLAS,
like R.
My question is, can I expect any significant speed-up from an Rcpp
re-implementation in this case, given it is already mostly matrix
algebra (which are supposed to be pretty efficient in R)?
Thanks,
Xavier
This very much depends on the code but there is a good chance that
RcppArmadillo will generate code making less data copies, etc ...
Hard to say without seeing the code.
Romain
--
Romain Francois
Professional R Enthusiast
+33(0) 6 28 91 30 30
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