3 ms·
One thing is having a bunch of computers. Another thing is to get the working efficiently on the same problem. While I'm not sure exascale was something like t
by magicalhippo 3y ago
One thing is having a bunch of computers. Another thing is to get the working efficiently on the same problem.
While I'm not sure exascale was something like the sound barrier, I do know a lot of hard work has been done to be able to efficiently utilize such large clusters.
Especially the interconnects and network topology can make a huge difference in efficiency[1] and Cray's Slingshot interconnect[2], used in Aurora[3], is an important part of that[4].
[1]: https://www.hpcwire.com/2019/07/15/super-connecting-the-supercomputers-innovations-through-network-topologies/ https://www.hpcwire.com/2019/07/15/super-connecting-the-supe...
[2]: https://www.nextplatform.com/2022/01/31/crays-slingshot-interconnect-is-at-the-heart-of-hpes-hpc-and-ai-ambitions/ https://www.nextplatform.com/2022/01/31/crays-slingshot-inte...
[3]: https://www.alcf.anl.gov/aurora https://www.alcf.anl.gov/aurora
[4]: https://arxiv.org/abs/2008.08886 https://arxiv.org/abs/2008.08886
- CyberDildonics 3y ago[flagged]
- magicalhippo 3y agoI specifically said I wasn't sure it's a barrier. My point was that you can't scale up without hard work. That is, you can't scale just by buying more of the same hardware.
- CyberDildonics 3y agoI specifically said I wasn't sure it's a barrier. Why would it be? That is, you can't scale just by buying more of the same hardware. What did the other super computers get wrong that made them slower?
- deleted 2y ago[deleted]
- hi-v-rocknroll 2y agoDiagonalization of working set and scaling-up and -out coordination. Some programs (algorithms) just have >= O(n) time and space, temporal-dependent "map" or "reduce" steps that require enormous amounts of "shared" storage and/or "IPC".