Is R more heavy on memory or processor?

Dear all,
 
I am going to purchase a Power Mac (a new one, with Nehalem processor) for my R-based microarray analyses. I use mainly Bioconductor packages, and a typical dataset would consist of 50 microarrays with 40,000 datapoints each. To make the right choice of processor and memory, I have a few questions:
 
- would the current version of R benefit from the 8 cores in the new Intel Xeon Nehalem 8-core Mac Pro? So would an 8-core 2.26GHz machine be better than a 4-core 2.93GHz? Or can R only use one core (in which case the 4-core 2.93GHZ machine would be better)?
 
- If R does not benefot from multiple cores yet, is there anything known about whether Snow Leopard might make a difference in this?
 
- To determine if my first priority should be processor speed or RAM, on which does R rely more heavily?
 
- The new chipset has 3 memory channels (forgive me if I word this wrong, as you may have noticed I am no computer tech) so it can read 6Gb RAM faster than it can read 8Gb of RAM; so for a program that relies more on RAM speed than RAM quantity it is recommended to use 6Gb instead of 8 for better performance (or any multiple of 3). Which is more important for R, RAM speed or RAM quantity?
 
(I am not sure if it helps to know, but previously I used a Powermac G5 quadcore (sadly I forgot which processor speed but it was the standard G5 quadcore) with 4 Gb RAM for datasets of 30-40 microarrays of 18,000 datapoints each, and analysis was OK except for some memory errors in a script that used permutation analysis; but it wasn't very fast.)
 
Any recommendations are welcome!
 
Marije Booman


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