Re:Compute Engine machine types with up to 96 vCPUs and 624G...
by neutronicus on Friday October 06, 2017 @09:29AM
(#15416602)
Is this really a better solution than just getting hours on NERSC (or some other government supercomputer)?
Re:Compute Engine machine types with up to 96 vCPUs and 624G...
by tallanvor on Friday October 06, 2017 @12:03PM
(#15417859)
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.
Re:Compute Engine machine types with up to 96 vCPUs and 624G...
by neutronicus on Friday October 06, 2017 @01:23PM
(#15418503)
...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.
Re:Compute Engine machine types with up to 96 vCPUs and 624G...
by mahdix on Friday October 06, 2017 @07:56AM
(#15416046)
That's interesting. What tools do you use to run your computations in parallel on a cluster? Hadoop?
Re:Compute Engine machine types with up to 96 vCPUs and 624G...
by malux85 on Friday October 06, 2017 @07:59AM
(#15416060)
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)
Re:Compute Engine machine types with up to 96 vCPUs and 624G...
by make3 on Friday October 06, 2017 @07:46AM
(#15415994)
how does the problem fair on gpus
Re:Compute Engine machine types with up to 96 vCPUs and 624G...
by hyperbovine on Friday October 06, 2017 @07:54AM
(#15416031)
If it requires synchronizing across workers, poorly.
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