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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 repl

by 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.

by davidmr 13y ago

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.