6 ms·
I know, I actually read the paper like a year ago. Actually, what was turned later into Amdahl's law is more or less a side-note in the paper :) The parent sai
by CookWithMe 14y ago
I know, I actually read the paper like a year ago. Actually, what was turned later into Amdahl's law is more or less a side-note in the paper :)
The parent said "most parallel programs", which I translated to embarrassingly parallel problems.
> But "real" supercomputers and clusters will spend the most money to reduce bottlenecks precisely because they want to run those programs that are the most limited by them.
I agree, but from the talks I have heard most time (= money) is spent trying to work around these bottlenecks as good as possible in software (i.e. incremental algorithms, pipelining, ...) to keep the machines busy. Then again, I hear a lot more talks about software than hardware, so you may be very right. If you have any particular example in mind, I would love to read about it!
- marshray 14y agoMy impression (formed from growing up as the annoying kid in the datacenter back when they let annoying kids hang out in the datacenter) is that buying new supercomputer hardware takes takes a lot of money and a really long time. Showing a return for the big investment might take much longer than the life of a typical PC. Once the a computer is actually in place (especially in a University setting), the interesting work begins, trying to write the best code for it and even improve the existing algorithms. It seems like there are computer purchases motivated by "this machine will run our existing code faster" and those motivated by "this machine will allow us to write code for it that will prove something faster". A supercomputer seems, almost by definition, the latter.