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Simulation and testing software for scheduling algorithms with the imprecise computation model for real-time AI.
This kind of mistake is unacceptable!
"""Methods to describe how we will tasks will be ran by the GPU""".
Like who wrote this? Probably @obroadrick
We need a better way to run benchmarks and develop.
We would need the following:
We wrote out the diagrams on the board, it would be nice to have them in a readable way online.
In order to simulate a NN we should do some GPU workload. Currently we are thinking of using different sizes of NxN matrix multiplication.
We should also narrow down how different sizes are treated as imprecise and what defined precision/error.
We need to be able to explain how our scheduling framework works. We would need the following:
Use HTTP instead of sockets for client server communication
We should think about how to show our algorithm is optimal.
For right now I think this means
i.e make delta MAX_REWARD / 10 (or some variable dividend choice)
so add 'divisions' as an input and then compute delta accordingly
Oliver already wrote the scheduler. Once we get an API for that we should be able to integrate with the server/GPU.
Client should send an array of tasks instead of just a single task
[]get_time_table(task t) //return range(0, t.task.length)
[]get_precision_table(task t) //return range(0, 100) (increment by t.task.length / 100) mess with later
It would serve as good automation and help if we could have automation for installing CUDA on our benchmark machine. As well as the other python dependencies.
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