Do everything in Java...

[John Colvin via Digitalmars-d]

On Friday, 5 December 2014 at 21:21:49 UTC, Ola Fosheim Grøstad 
wrote:
> On Friday, 5 December 2014 at 20:32:54 UTC, H. S. Teoh via 
> Digitalmars-d wrote:
>> I agree. It's not just about conservation of resources and 
>> power,
>> though. It's also about maximizing the utility of our assets 
>> and
>> extending our reach.
>>
>> If I were a business and I invested $10,000 in servers, 
>> wouldn't I want
>> to maximize the amount of computation I can get from these 
>> servers
>> before I need to shell out money for more servers?
>
> Those $10,000 in servers is a small investment compared to the 
> cost of the inhouse IT department to run them… Which is why the 
> cloud make sense. Why have all that unused capacity inhouse 
> (say >90% idle over 24/7) and pay someone to make it work, when 
> you can put it in the cloud where you get load balancing, have 
> a 99,999% stable environment and can cut down on the IT staff?
>
>> There are also certain large computational problems that 
>> basically need
>> every last drop of juice you can get in order to have any 
>> fighting
>> chance to solve them.
>
> Sure, but then you should run it on SIMD processors (GPUs) 
> anyway. And if you only run a couple of times a month, it still 
> makes sense to run it on more servers using map-reduce in the 
> cloud where you only pay for CPU time.
>
> The only situation where you truly need dedicated servers is 
> where you have real time requirements, a constant high load or 
> where you need a lot of RAM because you cannot partition the 
> dataset.

Big simulations still benefit from dedicated clusters. Good 
performance often requires uniformly extremely low latencies 
between nodes, as well as the very fastest in distributed storage 
(read *and* write).

P.S. GPUs are not a panacea for all hpc problems. For example, 
rdma is only a recent thing for GPUs across different nodes. In 
general there is a communication bandwidth and latency issue: the 
more power you pack in each compute unit (GPU or CPU or 
whatever), the more bandwidth you need connecting them.