Method
Cosmology@Home uses an innovative way of using machine learning to effectively parallelize a large computational task that involves many inherently sequential calculations over an extremely large number of distributed computers.
For any given class of theoretically possible models of the Universe, Cosmology@Home generates tens of thousands of example Universes and packages the cosmological parameters describing these Universes as work units. Each work unit represents a single Universe. When the work unit is requested by a participating computer, this computer simulates this Universe from the Big Bang until today. At the time of writing, each such simulation takes an average of 4 hours on a current CPU. The result of this simulation is a list of observable properties of this Universe.
This result is then sent back and archived at the Cosmology@Home server. When a sufficient number of example Universes have been simulated, a machine learning algorithm called Pico, which was developed by the project scientists of Cosmology@Home for this purpose, learns from these example calculations how to do the simulation for any Universe similar to the example Universes. The difference is that Pico takes a few milliseconds per calculation rather than several hours. Training Pico on 20,000 examples takes about 30 minutes. Once Pico is trained, it can run a full comparison of the class of models (which involves hundreds of thousands of model calculations) with the observational data in a few hours on a standard CPU.
The Cosmology@Home application is proprietary.
Read more about this topic: Cosmology@Home
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