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At Nerdalize we're building a cloud that's specifally build for high performance scientific compute: http://www.nerdalize.com/cloud/ http://www.nerdalize.com/cl

At Nerdalize we're building a cloud that's specifally build for high performance scientific compute: http://www.nerdalize.com/cloud/ http://www.nerdalize.com/cloud/

gumoro 10y ago
You might want to run a spell check on that page, I just saw "comprimise" in the most prominent sentence. Also, white text on sky background with white patches (sun, clouds) has unreadable portions.
- maaikest 10y ago
  Thanks for the comment, we fixed the typo. Any ideas on the content of the website are also very welcome.
  - axiom92 10y ago
    "Widget not in any sidebars". Neat idea. Reminds me of something I did a while back: http://www.journalrepository.org/media/journals/BJMCS_6/2014/Jul/Singh4202014BJMCS10885_1.pdf http://www.journalrepository.org/media/journals/BJMCS_6/2014.... We used idle systems in the computer lab to build large code repositories.
  - inlineint 10y ago
    This MBP model is a little bit dated and the photo is blurry: http://www.nerdalize.com/wp-content/uploads/2016/06/mockup-e1473688033532.png http://www.nerdalize.com/wp-content/uploads/2016/06/mockup-e...
danpalmer 10y ago
I never got into high performance scientific computing, but I believe the stuff that was done in my department at university was all MPI based and required very high interconnect speeds (like with Infiniband). It looks like your offering is much more standard, what's the thinking there, or am I just wrong/out of date?
- j-pb 10y ago
  It depends heavily on the kind of work. If you have a large scale simulation that needs to be partitioned like a weather system you are IO bound and need as thick interconnects as possible. However there are some problems which are very hard computationally but not very large. Basically everything in NP and exp is a good candidate. There you can distribute the same problem to a bazillion systems with a different starting configuration and let them run until one of them obtains a solution. If you look at the BOINC project those are basically all problems of this kind. Folding proteins like folding@home does for example. The description of a protein is fairly small, a couple megabyte max. However it takes a long time to simulate the behaviour, since chemistry is a messy probabilistic process with lots of back and forth. Nature does this on trillions of proteins at the same time within nanoseconds, and while we cannot reasonably increase the simulation speed of an individual protein, we can at least simulate as many proteins at once as possible.
- dekhn 10y ago
  An important secret in HPC is that MPI is rarely required to achieve your objectives. In many ways, vendors just use MPI as a way to sell expensive systems. If you can find any way to make your system scale using threads on a single machine, or use non-latency-sensitive networking, do so.
  - apathy 10y ago
    Or rent a bigger AWS/EC2 instance to prepare for the eventual demise of old school HPC
  - stonogo 10y ago
    If you don't need a high-speed interconnect, you don't need HPC. That's not to say that MPI per se must always be involved, but if for instance the 10gbit connection on Amazon's half-baked "HPC" offering is sufficient, then you definitely don't need a supercomputer. There is a ton of important scientific work waiting for core hours that really shouldn't be. A loosely-connected grid of laptops would serve a lot of projects very well. On the other hand, there is a large body of work that does require a classical supercomputer, so it doesn't really do anyone any good to accuse MPI of being a sales gimmick.
j-pb 10y ago
I love that you work on computational heating. Have you thought about open sourcing your heater design like back-blaze does with their storage pods? I tried really hard to get fiber but Ziggo 300 is the best one can do here sadly, so I build my own heating rigs that run folding@home 24/7. But I'd guess I'm not the only one interested in heating their home with science, so maybe you could gather a crowd of computational heating enthusiast and diy around you and learn from one another!
metaobject 10y ago
I don't understand what this means: "Great chance that it is cost efficient to run your job on our servers. Our servers are distributed over homes, so you don’t have to pay for the overhead of a datacenter. This means that your cost-per-job is up to 55% lower and you compute sustainably, as we use the produced heat to heat homes." Distributed over homes? As in "houses"? Your customer's data is stored at someone's (an employee's?) house?
- vmarsy 10y ago
  If this is similar to Qarnot[1], the servers are also heaters in people home. I'm not sure how the Internet connection data transfers are handled by the ISP [1] https://www.qarnot.com/qrad/ https://www.qarnot.com/qrad/
- shoyer 10y ago
  It looks like they install (sell?) racks of computers as household "heaters". Scroll down to the "Win, win, win!" section on their homepage with a video. This is a cute idea but I am skeptical that it makes sense from either an economic or environmental perspective. There are far more efficient ways to produce heat than electric heaters that run 24/7, and likewise cooling in data centers can be extremely efficient by making use of water, e.g., see https://www.google.com/about/datacenters/efficiency/internal/#water-and-cooling https://www.google.com/about/datacenters/efficiency/internal... Also, maintaining servers in people's homes must be quite expensive and there is limited capacity. It's hard to see that scaling. advanderveer -- do you have some sort of white-paper that compares the alternatives? Disclaimer: I work for Google, but not on Google Cloud.
  - j-pb 10y ago
    > There are far more efficient ways to produce heat than electric heaters that run 24/7. Do you mean cheaper? Because generating heat always has 100% efficiency. The only difference is that if you go from burnable materials to heat directly you don't get the nice side effect of getting computation done, so burning stuff is actually less efficient.

At Nerdalize we're building a cloud that's specifally build for high performance scientific compute: http://www.nerdalize.com/cloud/ http://www.nerdalize.com/cloud/

gumoro 10y ago

You might want to run a spell check on that page, I just saw "comprimise" in the most prominent sentence. Also, white text on sky background with white patches (sun, clouds) has unreadable portions.

maaikest 10y ago
Thanks for the comment, we fixed the typo. Any ideas on the content of the website are also very welcome.
- axiom92 10y ago
  "Widget not in any sidebars". Neat idea. Reminds me of something I did a while back: http://www.journalrepository.org/media/journals/BJMCS_6/2014/Jul/Singh4202014BJMCS10885_1.pdf http://www.journalrepository.org/media/journals/BJMCS_6/2014.... We used idle systems in the computer lab to build large code repositories.
- inlineint 10y ago
  This MBP model is a little bit dated and the photo is blurry: http://www.nerdalize.com/wp-content/uploads/2016/06/mockup-e1473688033532.png http://www.nerdalize.com/wp-content/uploads/2016/06/mockup-e...

danpalmer 10y ago

I never got into high performance scientific computing, but I believe the stuff that was done in my department at university was all MPI based and required very high interconnect speeds (like with Infiniband). It looks like your offering is much more standard, what's the thinking there, or am I just wrong/out of date?

j-pb 10y ago
It depends heavily on the kind of work. If you have a large scale simulation that needs to be partitioned like a weather system you are IO bound and need as thick interconnects as possible. However there are some problems which are very hard computationally but not very large. Basically everything in NP and exp is a good candidate. There you can distribute the same problem to a bazillion systems with a different starting configuration and let them run until one of them obtains a solution. If you look at the BOINC project those are basically all problems of this kind. Folding proteins like folding@home does for example. The description of a protein is fairly small, a couple megabyte max. However it takes a long time to simulate the behaviour, since chemistry is a messy probabilistic process with lots of back and forth. Nature does this on trillions of proteins at the same time within nanoseconds, and while we cannot reasonably increase the simulation speed of an individual protein, we can at least simulate as many proteins at once as possible.
dekhn 10y ago
An important secret in HPC is that MPI is rarely required to achieve your objectives. In many ways, vendors just use MPI as a way to sell expensive systems. If you can find any way to make your system scale using threads on a single machine, or use non-latency-sensitive networking, do so.
- apathy 10y ago
  Or rent a bigger AWS/EC2 instance to prepare for the eventual demise of old school HPC
- stonogo 10y ago
  If you don't need a high-speed interconnect, you don't need HPC. That's not to say that MPI per se must always be involved, but if for instance the 10gbit connection on Amazon's half-baked "HPC" offering is sufficient, then you definitely don't need a supercomputer. There is a ton of important scientific work waiting for core hours that really shouldn't be. A loosely-connected grid of laptops would serve a lot of projects very well. On the other hand, there is a large body of work that does require a classical supercomputer, so it doesn't really do anyone any good to accuse MPI of being a sales gimmick.

j-pb 10y ago

I love that you work on computational heating. Have you thought about open sourcing your heater design like back-blaze does with their storage pods? I tried really hard to get fiber but Ziggo 300 is the best one can do here sadly, so I build my own heating rigs that run folding@home 24/7. But I'd guess I'm not the only one interested in heating their home with science, so maybe you could gather a crowd of computational heating enthusiast and diy around you and learn from one another!

metaobject 10y ago

I don't understand what this means: "Great chance that it is cost efficient to run your job on our servers. Our servers are distributed over homes, so you don’t have to pay for the overhead of a datacenter. This means that your cost-per-job is up to 55% lower and you compute sustainably, as we use the produced heat to heat homes." Distributed over homes? As in "houses"? Your customer's data is stored at someone's (an employee's?) house?

vmarsy 10y ago
If this is similar to Qarnot[1], the servers are also heaters in people home. I'm not sure how the Internet connection data transfers are handled by the ISP [1] https://www.qarnot.com/qrad/ https://www.qarnot.com/qrad/
shoyer 10y ago
It looks like they install (sell?) racks of computers as household "heaters". Scroll down to the "Win, win, win!" section on their homepage with a video. This is a cute idea but I am skeptical that it makes sense from either an economic or environmental perspective. There are far more efficient ways to produce heat than electric heaters that run 24/7, and likewise cooling in data centers can be extremely efficient by making use of water, e.g., see https://www.google.com/about/datacenters/efficiency/internal/#water-and-cooling https://www.google.com/about/datacenters/efficiency/internal... Also, maintaining servers in people's homes must be quite expensive and there is limited capacity. It's hard to see that scaling. advanderveer -- do you have some sort of white-paper that compares the alternatives? Disclaimer: I work for Google, but not on Google Cloud.
- j-pb 10y ago
  > There are far more efficient ways to produce heat than electric heaters that run 24/7. Do you mean cheaper? Because generating heat always has 100% efficiency. The only difference is that if you go from burnable materials to heat directly you don't get the nice side effect of getting computation done, so burning stuff is actually less efficient.