Modelling: Build imprecise supercomputers

Abstract

Energy-optimized hybrid computers with a range of processor accuracies will advance modelling in fields from climate change to neuroscience, says Tim Palmer.T oday'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. MORE WITH LESSTake climate change, for example. Estimates of Earth's future climate are based on solving nonlinear (partial differential) equations for fluid flow in the atmosphere and oceans. Current climate simulators -typically with grid cells of 100 kilometres in width -can resolve the large, low-pressure weather systems typical of mid-latitudes, but not individual clouds. Yet modelling cloud systems accurately is crucial for reliable estimates of the impact of anthropogenic emissions on global temperature 1 .The resolution of this computational grid is determined by the available computing power. Current petaflop computers can perform up to 10 15 additions or multiplications -floating-point operationsper second (flops). By the early 2020s, next-generation exaflop supercomputers, capable of 10 18 operations per second, will be able to resolve the largest and most vigorous types of thunderstorm 2 . But cloud physics on scales smaller than a grid cell will still have to be approximated, or

Modelling Large, Dynamic, and Heterogeneous Populations Using DNA Libraries

“…In the combination of full-length B', the polymerization initiated by the short primer (A 12 , A 15 , or A 18 ) will become the rate-limiting step. Using a single DNA as a template, B' and A 18 were similarly efficient, thus the combination did not affect the RT-PCR curves remarkably (Fig. 2i).…”

“…The difference in diversity among populations does not necessarily fall into a wide range. The difference of RT-PCR curve among X 4 , X 6 and X 8 or among X 10 , X 12 and X 14 is relatively small, while X 16 , X 18 and X 20 cannot be distinguished from each other. The difference can be augmented through tuning the elongation temperature.…”

Crystal balls into the future: are global circulation and water balance models ready?

“…In an anticipation that the computing power needed to enable GCM modelers to carry out computations at significantly higher spatial and temporal resolution (that are viewed as the key in improving GCM performance) is still decades away, some scientist are envisioning a possible alternative pathways by using supercomputers that are less accurate and don't necessary compute the same results from identical input data (Palmer, 2015). It is unclear, how such model can be Projections of rel.…”

Using a PCR‐Based Method To Analyze and Model Large, Heterogeneous Populations of DNA

“…Although the method resembles that of DNA‐based computations with respect to parallel computing, it functions as an analogue computer with RT‐PCR curves as outputs. Compared with some of the most complex designs of DNA computing, with tens to hundreds of unique DNA strands, this process can work with datasets of extraordinarily large size, such as the X 20 library, which contains more than one trillion sequences.…”