Message-Id: <3512B5F0.5C12FCE0@census.gov> Date: Fri, 20 Mar 1998 13:31:12 -0500 From: Rob Creecy <rcreecy@census.gov> To: David Clayton <david.clayton@mrc-bsu.cam.ac.uk> Subject: Re: R-beta: Sun performance library I looked at this a little last fall after noticing that matrix multiplication performance in R was far below that achievable performance in FORTRAN or C using the BLAS and LAPACK. I've been looking for notes on this but can't find them. My recollection was that roughly speaking I could get 100MFLOPS on a SPARC ULtra 2300 single processor for matrix multiply for matrices bigger than 100x100 using FORTRAN, while I was getting less than 10MFLOPS in R (or SPLUS). These numbers may not be accurate so take them with a grain of salt. I then did a very quick test of trying to link the BLAS DGEMM matrix multiply routine with R. I only remember the bottom line: it wasn't worth pursuing further because even though the matrix multiply in FORTRAN was sped up considerably, there was still a lot of overhead passing data back and forth between R and the FORTRAN routine. It is possible that for very large problems there may still be an advantage of linking an optimized BLAS or LAPACK routine since execution time should be O(n^3) while time spent passing matrices as arguments should be O(n^2) - it just wasn't worth it for the problems I was trying. Another point - I found that compiling the BLAS and LAPACK source code available from NETLIB gave the about the same results as the "optimized" SUN Performance Library routines, so using the SUN routines just saves time figuring out how to compile them. I never did finish trying to do the link with R since it didn't seem worth the effort. Rob Creecy Census Bureau David Clayton wrote: > > Has anyone tried to build R on Suns by linking to the versions of BLAS and > LINPACK in the Sun Performance Library rather than the standard versions > included in the distribution? On the face of it this would seem to be a highly > desirable thing to do since these versions of the routines are more efficient > and can exploit parallelism on multiprocessor machines. > -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request@stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._