On 07/02/12 11:31, Alaios wrote:
I would like to thank you Ernest for your answer. I guess that this
is gonna be faster as right now R only sees one core. In my work
there is a system with 64 cores and you can see only one working. If
I understand it right a [m,n][n,k] matrix multiplication can be split
into rows (from first matrice) and columns (from the second matrice)
and then combine all the local results of each cpu together.
You definitaly can go this way, but I would STRONGLY recommend to search
for "parallel BLAS", check in the R-admin manual the section "Linear
Algebra" which deals with BLAS et al, and e.g.
http://www.r-bloggers.com/compiling-64-bit-r-2-10-1-with-mkl-in-linux/
My guess is that a paralelization on the C level in the BLAS et al.
library will be MUCH faster then a paralelization on R level.
Also, there is a R-sig-hpc mailing list for these kind of questions.
Cheers,
Rainer
Would that be too weird for mclapply to handle?
B.R Alex
________________________________ From: Ernest
Adrogué<nfdi...@gmail.com> To: r-help@r-project.org Sent: Tuesday,
February 7, 2012 11:02 AM Subject: Re: [R] using mclapply (multi core
apply) to do matrix multiplication
7-02-2012, 00:29 (-0800); Alaios escriu:
Dear all, I am trying to multiply three different matrices and
each matrice is of size 16384,16384 the normal %*% multiplciation
operator has not finished one day now. As I am running a system
with many cores (and it seems that R is using only one of those) I
would like to write fast a brief function that converts the typical
for loops of a matrix multiplication to a set of lapply sets
(mclapply uses the lapply syntax but it applies the work to many
cores).
If my thinking is correct , in the sense that this will speed up
things a lot, I want you to help me covert the first matrix in
rows the second in columns and convert those in a format that
lapply would like to work with.
If I understand correctly, R uses a specialized library called BLAS
to do matrix multiplications. I doubt re-implementing the matrix
multiplication code at R-level would be any faster. What you can try
is replace BLAS with a multicore version of BLAS although it's not
easy if you have to compile it yourself.
Also, you may try to re-think the problem you're trying to solve.
Maybe there's a different approach that is less
computation-intensive.
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