Modelling: Build imprecise supercomputers
Energy-optimized hybrid computers with a range of processor accuracies will advance modelling in fields from climate change to neuroscience, says Tim Palmer.
Today's supercomputers lack the power to model accurately many aspects of the real world, from the impact of cloud systems on Earth's climate to the processing ability of the human brain. Rather than wait decades for sufficiently powerful supercomputers — with their potentially unsustainable energy demands — it is time for researchers to reconsider the basic concept of the computer. We must move beyond the idea of a computer as a fast but otherwise traditional 'Turing machine', churning through calculations bit by bit in a sequential, precise and reproducible manner.
In particular, we should question whether all scientific computations need to be performed deterministically — that is, always producing the same output given the same input — and with the same high level of precision. I argue that for many applications they do not.
Energy-efficient hybrid supercomputers with a range of processor accuracies need to be developed. These would combine conventional energy-intensive processors with low-energy, non-deterministic processors, able to analyse data at variable levels of precision. The demand for such machines could be substantial, across diverse sectors of the scientific community.
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