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> where am I suppose to work if, to produce meaningful results, you need way-too-expensive hardware... I know how you feel, but I also think stories like this
by thomasahle 5y ago
> where am I suppose to work if, to produce meaningful results, you need way-too-expensive hardware...
I know how you feel, but I also think stories like this may be a wake-up call for some groups to invest more in hardware. The Baker and Tsinghua University groups are not small. They can afford more than 4 GPUs.
Probably it's more about setting up a good pipeline. Once you get about 4 GPUs you need more than one machine to run them. Hopefully in the next years we'll see more open source tools to make it easy to "build your own GPU cloud".
- dumb1224 5y agoAnd to use all that GPU power exclusively you need some good strategy too. Speaking from a medium size biology research centre.
- thomasahle 5y agoTrue. It may be that a smaller research center will only rarely be able to saturate the cluster. Maybe it doesn't matter, like how other lab equipment is not in constant use. Another option may be for centers to team up and have shared machines? Or maybe compute as a service will eventually be cheap enough for this not to matter...
- dumb1224 5y agoCurrently only those team who are heavy-deep-learning got special exclusive queues on the GPU nodes on the cluster. If many users want to use the GPUs at the same time it might need some planning. I don't know if it is a solved problem in the HPC field though.