Exponential computing power increase = linear performance improvement

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funkervogt
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Exponential computing power increase = linear performance improvement

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This paper shows that, in the areas of Computer Chess, Computer Go, Weather Prediction, Protein Folding and Oil
Exploration, progress depends on getting exponential increases in the amount of computing power. We propose that
this is likely a broader phenomenon, because the computational techniques used for these areas are also used in many
others. In particular, we suggest that progress in science and engineering often requires finer approximations, more
complicated evaluation, repeated analysis under different starting conditions, greater search depth, and more degrees
of freedom. Support for this view is provided by Hyperion, a computing research firm, that gathered 690 examples
of high-performance computing. They found that investment in High Performance Computing significantly improve
economic success and increase scientific innovation across many areas [90].

Across our case study areas, Computer Chess, Computer Go, Weather Forecasting, Oil Exploration (BP), and Protein
Folding (CASPs), we find that computing power has increased 140×, 390,000,000×, 1,600,000×, 160,000×, 58,000×
respectively, representing yearly compound growth rates of 14 − 90%.

Fortunately for the procurers of these systems, costs have not risen proportionally to these increases, principally because
Moore’s Law provided ever-cheaper computing power.

...Overall, this paper paints a coherent picture of computing power improvements as a central driver of progress across
many areas over decades, quantifying long-held views about the centrality of I.T. in general, and of Moore’s Law in
particular as a driver of long-term performance improvement across society. It shows the importance of exponentially
more computing power, and raises the specter of what we would lose absent that growth.
https://arxiv.org/pdf/2206.14007.pdf
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