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An article from Ars Technica [1] on this topic makes the following point: "Unlike most Azure compute resources, which are typically shared between customers, t

by dhd415 9y ago

An article from Ars Technica [1] on this topic makes the following point: "Unlike most Azure compute resources, which are typically shared between customers, the Cray supercomputers will be dedicated resources. This suggests that Microsoft won't be offering a way of timesharing or temporarily borrowing a supercomputer. Rather, it's a way for existing supercomputer users to colocate their systems with Azure to get the lowest latency, highest bandwidth connection to Azure's computing capabilities rather than having to have them on premises." Somewhat interestingly, this sounds like a bit like hybrid cloud except that it's hosted entirely in Azure datacenters rather than partially on-premises. [1] https://arstechnica.com/gadgets/2017/10/cray-supercomputers-coming-to-azure-cloud/ https://arstechnica.com/gadgets/2017/10/cray-supercomputers-...

trhway 9y ago
Interesting what tasks require both, supercomputer and normal systems, and for them to be tightly connected. Until of course it is consultant BS/upsell trying to cover/workaround performance issues elsewhere in the stack.
rbanffy 9y ago
They still could be shared on a job by job base. For jobs that need the ridiculously fast interconnects (large datasets to be worked on by thousands of cores) you go Cray, when they don't, you go commodity Azure. Think of it as a rack-sized GPU.
KGIII 9y ago
With distributed computing and cloud hosting allowing for thousands of instances, with huge amounts of resources such as obscene terabytes of RAM, is there still a need for supercomputers? They are, effectively, the same thing, right? I am guessing that I'm not understanding something fully. I don't really see the benefit anymore, now that you can lease thousands of cores, petabytes of disk, and multiple terabytes of RAM. What's the benefit? What am I missing? Google is none too helpful.
- jerven 9y ago
  Internode latency, when you have one big problem and communication is the bottleneck.
- justincormack 9y ago
  Fast low latency interconnects, and scale up versions of the resources (eg lots of gpus per box) to minimise communication overhead.
- cwingrav 9y ago
  Parallel processing is about network latency and bandwidth for the types of algorithms that don't divide into small computable/paralelizable bits easily. For those tasks, supercomputer buses are unmatched.
  - KGIII 9y ago
    That makes some sense, so I'll just post my thanks in this one reply so I don't have to thank everyone individually. Much appreciated. It does lead me to one additional question - is the need for additional speed great enough to justify this? I don't know how much faster it would be and I tried Google and they are not even remotely helpful. I may just be using the wrong query phrases.
    - Xcelerate 9y ago
      > is the need for additional speed great enough to justify this? Yep. I did molecular dynamics simulations on the Titan supercomputer, and also tried some on Azure's cloud platform (using Infiniband). The results weren't even close.
      - KGIII 9y ago
        When you say the results weren't even close, and if you have time and don't mind, could you share some numbers/elaborate on that? My experience with HPC is fairly limited, compared to what I think you're discussing. In my case, it was things like blade servers which was a cost decision. We also didn't have the kind of connectivity and speeds that you have available today. (I modeled traffic at rather deep/precise levels.) So, if you have some experience numbers AND you have the free time, I'd love to learn more about the differences in the results? Were the benefits strictly time? If so, how much time are we talking about? If you had to personally pay the difference, which one would you select? Thanks for giving me some of your time. I absolutely appreciate it.

by dhd415 9y ago

trhway 9y ago

Interesting what tasks require both, supercomputer and normal systems, and for them to be tightly connected. Until of course it is consultant BS/upsell trying to cover/workaround performance issues elsewhere in the stack.

rbanffy 9y ago

They still could be shared on a job by job base. For jobs that need the ridiculously fast interconnects (large datasets to be worked on by thousands of cores) you go Cray, when they don't, you go commodity Azure. Think of it as a rack-sized GPU.

KGIII 9y ago

With distributed computing and cloud hosting allowing for thousands of instances, with huge amounts of resources such as obscene terabytes of RAM, is there still a need for supercomputers? They are, effectively, the same thing, right? I am guessing that I'm not understanding something fully. I don't really see the benefit anymore, now that you can lease thousands of cores, petabytes of disk, and multiple terabytes of RAM. What's the benefit? What am I missing? Google is none too helpful.

jerven 9y ago
Internode latency, when you have one big problem and communication is the bottleneck.
justincormack 9y ago
Fast low latency interconnects, and scale up versions of the resources (eg lots of gpus per box) to minimise communication overhead.
cwingrav 9y ago
Parallel processing is about network latency and bandwidth for the types of algorithms that don't divide into small computable/paralelizable bits easily. For those tasks, supercomputer buses are unmatched.
- KGIII 9y ago
  That makes some sense, so I'll just post my thanks in this one reply so I don't have to thank everyone individually. Much appreciated. It does lead me to one additional question - is the need for additional speed great enough to justify this? I don't know how much faster it would be and I tried Google and they are not even remotely helpful. I may just be using the wrong query phrases.
  - Xcelerate 9y ago
    > is the need for additional speed great enough to justify this? Yep. I did molecular dynamics simulations on the Titan supercomputer, and also tried some on Azure's cloud platform (using Infiniband). The results weren't even close.
    - KGIII 9y ago
      When you say the results weren't even close, and if you have time and don't mind, could you share some numbers/elaborate on that? My experience with HPC is fairly limited, compared to what I think you're discussing. In my case, it was things like blade servers which was a cost decision. We also didn't have the kind of connectivity and speeds that you have available today. (I modeled traffic at rather deep/precise levels.) So, if you have some experience numbers AND you have the free time, I'd love to learn more about the differences in the results? Were the benefits strictly time? If so, how much time are we talking about? If you had to personally pay the difference, which one would you select? Thanks for giving me some of your time. I absolutely appreciate it.