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In my algorithms class I have a need to crunch a major amount of data. For consistency purposes, I cannot use my laptop. So I'm going to be installing Linux on a machine of mine to do this. I'll only be installing MySQL, GCC, and Java.
I haven't been keeping myself up to date on the emerging distributions out there. If this was a year ago, I would have just installed RedHat. Fedora I don't feel as comfortable with for whatever reason. I do like the idea of a distribution from Novell, but I haven't heard of any plans for a new release of SuSE. Can anybody help me out in picking one? I don't want to be too bogged down in configuration and installation. I like the ease of RedHat and I'm wondering if I could get the same out of Gentoo.
And please, when you make a suggestion, give a good reason as to why!
If all you're installing is what you've mentioned above, then it's not really going to make any difference which distro you choose.
Three things you might want to consider:
1) Ease of installation
2) GCC version (if you are doing strange and interesting things with your compiler, then you might need a particular version. I'd imagine you'd know if you needed to know)
3) Package format - if you want to go RedHat/Mandrake/SuSE, can you get the software you need as RPMs, if not can you get the packages as debian style packages etc)
For ease of installation, I would go for RedHat - as far as I know it's pretty much got the best hardware compatibility out-the-box. You're also very likely to get any software you need as RedHat 9 RPMs.
My experience using other distributions (I've used RedHat 7/8/9, Mandrake and Knoppix briefly) is limited, so I couldn't really compare directly.
I normally compile everything from source on my *nix machines, so package formats do not really matter.
I am going to be looking at more than just what I mentioned above, which includes using the machine like a desktop. I'm not actually going to use it as a main machine, but I would like to play around with it and see how I'd like it as one. However, the main purpose of this is to crunch very large amounts of data (i.e., 100 million+ rows in data structures, etc).