On Apr 18, 2007, at 8:28 AM, [EMAIL PROTECTED] wrote: > >> I am running R 2.4.1 on the new 8-core Mac Pro with the parSapply >> function from the Snow package. Tests using 2,4, and 8 threads with >> makeCluster() yield somewhat disappointing results. The 4 thread >> process >> is fastest. With 8 threads, all the cores max out at about 70% power, >> and even then it is slower than the 4 thread process which maxes >> out the >> 4 threads at about 90-95%. This suggests the additional 4 cores on >> the >> Mac Pro do not improve performance in an embarrassingly parallel R/ >> Snow >> environment... > >> The function I tested in parSapply runs a regression (a call to >> "lm"). I >> am using Snow/rpvm package combination. There is no issue of limited >> memory. There are over 3 gigs of free RAM in the tests. > > > There has been some discussion (elsewhere) of the mac pro > motherboard's inability to keep all eight cores supplied with data > from memory at an adequate rate. > > Your processor cores may simply be starved for data. The amount of > memory available doesn't matter, much, if there's no IO bandwidth > left between the CPUs and the DIMMs.................... >
The Intel's SMP design is poor compared to its competitors (not just the MoBo, the CPUs as well), that is a well known fact, but I wouldn't be so sure that it is what hits you. Can you try to run some some big BLAS operations like for example: set.seed(1) a<-matrix(rnorm(4000000),2000) b<-matrix(rnorm(4000000),2000) system.time(for (i in 1:20) a%*%b) This takes ca 14.2s on a 2.66 quad Mac Pro (46.5s user time). I wonder what the 8-core does with this. If you can't feed all 8 cores then I'd say it's likely a bandwidth issue ... Cheers, S _______________________________________________ R-SIG-Mac mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-mac
