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I wrote a different reply but deleted it. I'm curious about something: if, as you claim, supercomputers are wasted money, why are there so many of them? Have al

by davidmr 13y ago

I wrote a different reply but deleted it. I'm curious about something: if, as you claim, supercomputers are wasted money, why are there so many of them? Have all the world's top supercomputing sites somehow colluded to convince all the world's largest governments that they're useful?

stingraycharles 13y ago
I'm not disagreeing with you, but the argument of "people are using it and the government keeps paying for them" is hardly convincing.
- davidmr 13y ago
  It's not just the government that buy these things. Oil and gas, heavy manufacturing, automotive, genomics, financial, etc. all have them. The poster I was replying to appeared to be under the gross misconception that all parallel problems were more effectively solved with high throughput clusters.
  - dekhn 13y ago
    Can you point to a genomics company that uses a supercomputer? Adn really claims that its unique capabilities are necessary> Remember, the human genome was assembled on two different architectures: a Dec Alpha cluster with large memory with fast network (for the day) and a bunch of cheap linux machines with slow network. It turns out you didn't actually need the former, although the people at Celera insisted you did.
    - jerven 13y ago
      Genetics is normally very very data parallel. And often the ideal case for throughput computing. But there are rare times when doing modelling that a lot of chatter is required over interconnects. But even here people normally just get 4-6TB RAM machines instead of having lots of machines.
      - dekhn 13y ago
        yes, and the people buying those machines often tell me that their science is limited by how much RAM they could buy in a single machine. Which is poppycock.
        jerven 13y ago
        Oh, you could make this run using MPI etc... no problem. But then all you are really doing is going from high density to low density ram. And that is just not as large a saving as you would think it is. Because you are expanding rapidly the number of cpu's and racks you need to compensate for the less dense RAM. A good sequencing lab does not need more RAM than that per assembly. If you have a reference even less. Plus the science is hard enough that throwing mulitnode programming in the middle is tricky. Remember most of this code is written by a PhD student on his first real programming job. Its hard enough to go from getting it to run on something else than the PhD students laptop, never mind robustly parallelize this across nodes.
dekhn 13y ago
At this point, large capability supercomputers that solve problems we don't need solved are mostly kept around as trophy pieces with a few "show me" calculations. Note that when the Chinese obtained the leadership in TOP500 a lot of people got worried (as they did when Japan did it with the Earth Simulator). And so the US may spend a bunch of money to get on top of that list again. Big deal. It's LINPACK. Fortunately, TOP500 should eventually introduce better codes to benchmark. I'm sure there are scientists who put their all into getting their code to run on a super computer, then apply for a pittance of time and sit in a queue for weeks, but they're not publishing as many (or as interesting) papers as the ones who are pulling out their credit card and building mini-supercomputers on Amazon that rely on conventional interconnects and better algorithms and data processing tools. Don't assume I'm ignorant about what supercomputers are used for. I used to work on supercomputers; that includes writing, running and evaluating codes, and selecting proposals on some of the largest machines in the world (at the time). But these new approaches to doing science and engineering on large computer systems have obsoleted conventional supercomputing for all but an extremely limited set of computational problems. And that set of problems becomes smaller when clever computer scientists figure out smarter ways to run things on cheaper architectures. For example, page rank is a classic eigenvector problem; you can solve it by building a big matrix on your supercomputer and doing the appropriate calculations, using 50+ years of numerical optimization (but not really very good support for nodes failing between checkpoints). However, you can also implement it as an iterative mapreduce. The mapreduce checkpoints every little bit of map work, and along the way, handles those failures quite well. It can also handle data sets larger than the sum of RAM on the machines quite well. Guess which one works better operationally, scales to a larger data set size, and ports to a lot of architectures cheaply?
- tfgg 13y ago
  As far as I understand, you're saying that high-speed interconnects are largely a waste of time? (and apparently that doing protein MD is a waste of time, but that's a scientific question that I'm going to disagree with you on). I can imagine that might be the case for some simulations, but how I do, for example, a parallel FFT without significant communication? I personally see it more as there's a pretty limited set of computational problems which are low-communication, often requiring rather gross approximations, even with the "clever computer scientists" looking at the problem, and they're often not the problems scientists actually want to solve, and so dismissing supercomputing is rather ridiculous.

by davidmr 13y ago

stingraycharles 13y ago

I'm not disagreeing with you, but the argument of "people are using it and the government keeps paying for them" is hardly convincing.

davidmr 13y ago
It's not just the government that buy these things. Oil and gas, heavy manufacturing, automotive, genomics, financial, etc. all have them. The poster I was replying to appeared to be under the gross misconception that all parallel problems were more effectively solved with high throughput clusters.
- dekhn 13y ago
  Can you point to a genomics company that uses a supercomputer? Adn really claims that its unique capabilities are necessary> Remember, the human genome was assembled on two different architectures: a Dec Alpha cluster with large memory with fast network (for the day) and a bunch of cheap linux machines with slow network. It turns out you didn't actually need the former, although the people at Celera insisted you did.
  - jerven 13y ago
    Genetics is normally very very data parallel. And often the ideal case for throughput computing. But there are rare times when doing modelling that a lot of chatter is required over interconnects. But even here people normally just get 4-6TB RAM machines instead of having lots of machines.
    - dekhn 13y ago
      yes, and the people buying those machines often tell me that their science is limited by how much RAM they could buy in a single machine. Which is poppycock.
      - jerven 13y ago
        Oh, you could make this run using MPI etc... no problem. But then all you are really doing is going from high density to low density ram. And that is just not as large a saving as you would think it is. Because you are expanding rapidly the number of cpu's and racks you need to compensate for the less dense RAM. A good sequencing lab does not need more RAM than that per assembly. If you have a reference even less. Plus the science is hard enough that throwing mulitnode programming in the middle is tricky. Remember most of this code is written by a PhD student on his first real programming job. Its hard enough to go from getting it to run on something else than the PhD students laptop, never mind robustly parallelize this across nodes.

dekhn 13y ago

At this point, large capability supercomputers that solve problems we don't need solved are mostly kept around as trophy pieces with a few "show me" calculations. Note that when the Chinese obtained the leadership in TOP500 a lot of people got worried (as they did when Japan did it with the Earth Simulator). And so the US may spend a bunch of money to get on top of that list again. Big deal. It's LINPACK. Fortunately, TOP500 should eventually introduce better codes to benchmark. I'm sure there are scientists who put their all into getting their code to run on a super computer, then apply for a pittance of time and sit in a queue for weeks, but they're not publishing as many (or as interesting) papers as the ones who are pulling out their credit card and building mini-supercomputers on Amazon that rely on conventional interconnects and better algorithms and data processing tools. Don't assume I'm ignorant about what supercomputers are used for. I used to work on supercomputers; that includes writing, running and evaluating codes, and selecting proposals on some of the largest machines in the world (at the time). But these new approaches to doing science and engineering on large computer systems have obsoleted conventional supercomputing for all but an extremely limited set of computational problems. And that set of problems becomes smaller when clever computer scientists figure out smarter ways to run things on cheaper architectures. For example, page rank is a classic eigenvector problem; you can solve it by building a big matrix on your supercomputer and doing the appropriate calculations, using 50+ years of numerical optimization (but not really very good support for nodes failing between checkpoints). However, you can also implement it as an iterative mapreduce. The mapreduce checkpoints every little bit of map work, and along the way, handles those failures quite well. It can also handle data sets larger than the sum of RAM on the machines quite well. Guess which one works better operationally, scales to a larger data set size, and ports to a lot of architectures cheaply?

tfgg 13y ago
As far as I understand, you're saying that high-speed interconnects are largely a waste of time? (and apparently that doing protein MD is a waste of time, but that's a scientific question that I'm going to disagree with you on). I can imagine that might be the case for some simulations, but how I do, for example, a parallel FFT without significant communication? I personally see it more as there's a pretty limited set of computational problems which are low-communication, often requiring rather gross approximations, even with the "clever computer scientists" looking at the problem, and they're often not the problems scientists actually want to solve, and so dismissing supercomputing is rather ridiculous.