On Oct 20, 2010, at 2:18 PM, Michael Spiegel wrote: > Hi Mac Special Interest Group folks, > > We've noticed some curious behavior of the veclib BLAS implementation > in the development of the OpenMx library. The daxpy implementation > appears to be twice as slow in the veclib implementation as compared > to the reference implementation.
Yes, that is a known problem (see R-SIG-Mac archives). vecLib fails to use multiple cores on Nehalem-based Mac Pros. If you use ATLAS directly it will work just fine - that's what we recommend on Mac Pros. Cheers, Simon > Attached is a test kernel that has > been run under both implementations. The kernel consists of repeated > calls to daxpy with vectors of varying size. In the output files, the > first column is the dimension of the vector. The 2nd-4th column > report the runtime in seconds of the kernel; three identical trials > per vector size. > > It may be more appropriate to send this information upstream to the > veclib persons. However, I thought it would be of interest to Mac R > folks, too. For our own project, our workaround will be to create our > own basic implementation of daxpy, and continue to link against the > veclib BLAS library so we can get a speedup on dgemm and the other > functions. > > The benchmarks were executed on a Mac Pro with 2 Quad-Core Xeons @ 3 > GHz (MacPro2,1) running OS X 10.5.8. It was tested with R 2.12.0 and > the same behavior has been observed with R 2.10.1. > > Thanks, > --Michael > <omxTest.c><daxpy.veclib.results><daxpy.refblas.results>_______________________________________________ > R-SIG-Mac mailing list > R-SIG-Mac@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/r-sig-mac _______________________________________________ R-SIG-Mac mailing list R-SIG-Mac@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-mac