31–40 of 47 posts
Congrats guys this is a really neat hack, really impressive. Did you guys think about building it further out to provide a GPU load balancer for multiple frontend machines running Cuda / OpenCL?
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Somebody should make a startup that allows people to sell access to their computers by the minute. Like spot instances in the cloud ... in people's basements. The true sharing economy.
That sounds like a nightmare. You'd have zero uptime guarantees!
What if you limited people to writing in a domain specific language: one that ran distributed on this infrastructure? How would that make it different than folding at home, for example?
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Somebody should make a startup that allows people to sell access to their computers by the minute. Like spot instances in the cloud ... in people's basements. The true sharing economy.
Why not just move forward to realizing some sort of distributed/decentralized internet? Something like a combination of Freenet, TOR, BOINC, blockchain etc. technologies, using the current "legacy" internet as a backbone, where anyone can voluntarily offer their computing and storage resources to the network at varying levels of participation. Say you could offer your laptop as a simple discovery/directory node to si…
I wouldn't be surprised if certain services became more centralized, offloading computing to the cloud. Consumer grade devices would become thin clients (like back in the day). NVIDIA has hinted in this direction, I can remember something about GaaS (Gaming as a Service).
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Somebody should make a startup that allows people to sell access to their computers by the minute. Like spot instances in the cloud ... in people's basements. The true sharing economy.
That sounds like a nightmare. You'd have zero uptime guarantees!
Congrats guys this is a really neat hack, really impressive. Did you guys think about building it further out to provide a GPU load balancer for multiple frontend machines running Cuda / OpenCL?
Hmm, can you elaborate? Do you mean having multiple smaller instances talk to a single GPU instance?
Since the GPU client is now abstracted from the GPU devices by placing the GPUs across the network. It seams like time-sharing should be next logical step.
Earlier quoted context omitted.
Somebody should make a startup that allows people to sell access to their computers by the minute. Like spot instances in the cloud ... in people's basements. The true sharing economy.
That sounds like a nightmare. You'd have zero uptime guarantees!
I've found amazon GPU instances to be really expensive (even the spot prices have been high recently), especially if you need it for longer runs for deep learning. The other issue is that the additional layers of virtualization create bandwidth overhead issues. I'd like to see something in the cloud thats bare-metal / full access to GPUs (Maybe a good idea to start one). For scaling higher with a very large number of…
Somebody should make a startup that allows people to sell access to their computers by the minute. Like spot instances in the cloud ... in people's basements. The true sharing economy.
Earlier quoted context omitted.
Sounds like Plan9's concept of "CPU server mounts" has been reborn as "GPU server mounts." Could actually get traction this time, given that existing multi-GPU programs will Just Work.
I cant wait for company to provide opencl/cuda mflops as a service instead of giving you vms as a whole, so one could just attach remote engine to any smallish controller vm
We are not limiting our software to AWS so you can built this kind of service on any kind of cluster by installing our software directly from https://boost.bitfusion.io - I say cluster, because we have played with the idea of thin devices accessing remote GPU instances in the cloud, but over public networks the network performance was a limiting factor.
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Hmm, can you elaborate? Do you mean having multiple smaller instances talk to a single GPU instance?
I'm talking about time-sharing. It doesn't mater if it's smaller instances sharing a single GPU instance or many instances sharing many GPU instances. Essentially N:M sharing (with some scheduling). Since the GPU client is now abstracted from the GPU devices by placing the GPUs across the network. It seams like time-sharing should be next logical step.
Check it out: https://console.aws.amazon.com/cloudformation/home?region=us...