6 ms·

I wonder who will use these types of instances?

• malux85 9y ago
  scientific computing and simulation. I have had need for machines like this - for a project that was very compute heavy but embarrassingly parallel, but also was very "chatty" - I.e updated the data structures a lot during compute I can't be too specific about it, but if involved creating a very large tree structure and updating, pruning and transversing the tree a lot If the algorithm is updating and reading a large data structure a lot it's only practical from a speed point of view to hold the whole structure in RAM
  • make3 9y ago
    how does the problem fair on gpus
    • hyperbovine 9y ago
      If it requires synchronizing across workers, poorly.
      • malux85 9y ago
        This is correct
      • make3 9y ago
        depends how often you need to synchronize. look at deep learning, you need to sync the weights as often as possible
  • mahdix 9y ago
    That's interesting. What tools do you use to run your computations in parallel on a cluster? Hadoop?
    • malux85 9y ago
      For this type of problem - it must be done on a single node. The overhead of network communication would have killed the latency requirement - that's why we need a huge machine like this. The code was written in C (We also maintain a Hadoop and Cassandra cluster, and I use Spark for distributed computation - but those are different projects)
  • neutronicus 9y ago
    Is this really a better solution than just getting hours on NERSC (or some other government supercomputer)?
    • tallanvor 9y ago
      Private companies want to do simulations as well, and with this type of solution, you can pretty much run them on demand rather than having to wait in line.
      • neutronicus 9y ago
        ...I guess. My advisor's company straddles the public / private divide, but we've definitely done some simulations for private clients on NERSC, and I assume we weren't misusing hours allocated for some other purpose.