Oh. Then, why the fuss about Occam, Fortress, Hadoop, et al? Is there another, hairier area of HPC these languages and technologies are meant for, where scientists rarely need to tread? Or are most scientists deliberately avoiding those areas because of the hairiness?
i don't think its about hairiness. its about things like legacy code, speed/efficiency, knowledge base, etc..
fortress isn't ready yet, hadoop is java-based (which means slower performance and possible code rewrite), and occam can't jump through the same flaming hoops that C/fotran can (i think -- i'm going on word of mouth). these are all great tools for HPC, but i don't think that they line up with the needs of existing scientific programming. fortress will, though, most likely, once its ready.
Depends on what you want to do as well. Hadoop is not the appropriate solution for doing molecular dynamics simulations. You also have to look at the support system. Something like LAPACK makes Fortran very attractive for numerical programming.
What kinds of algorithms are you trying to implement?
For research, right now I'm looking for better ways to visualize the results of Chain analysis on a set of sequences. (This probably doesn't actually need a cluster.) We're looking at evolutionary relationships between protein families.
I just started a graduate program, so I'll have to do a range of things for classes -- which is why I have some leeway to pick up a new language and stagger through a few numerical programs now if it will help me get things done quickly later, when it matters.
It's probably worth your while to take a loop at Hadoop them. When you can fundamentally map your problem into many small problems and then reduce the results (true for most sequence alignment, etc problems), then Hadoop is very viable. Not exactly common fare these days, but getting more popular and perhaps increasingly so. Plus you don't really have to learn new languages, just a distributed computing framework.
