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> Because climate-change is eating the world. CPUs need power. Less CPU-usage equals less power-consumption. Faster code might result in more CPU-usage not le

> Because climate-change is eating the world. CPUs need power. Less CPU-usage equals less power-consumption. Faster code might result in more CPU-usage not less, because the amount of real world value derived by the same amount of electricity increases. That being said I'm all for more efficient & faster code.

verdverm 5y ago
Yea, I was thinking you can't really make a generalization like that. 1. Companies may do more with the same amount of CPU, more data, more calculations, despite per evaluation being faster. 2. Improved performance may make usage within reach of more end users 3. Improving widely used packages could reduce, especially those that are used in heavy calculations. With ML, we often see more improvement (reduced learning time) through algorithm research, so library improvements are only part of the story. Of course advances like Capsule Networks and encrypted learning sit in the queue because they are too expensive to compute today, but have great benefit to the outcomes.
- bee_rider 5y ago
  Also depends on what we're doing. Lots of computational tasks in engineering will take as many flops as you've got and produce a more accurate simulation. The hope would be that the end result would be a somehow better device (better fluid dynamics -> more aerodynamic car). On the other hand, we have things like advertisement which are more of a zero sum competition, don't produce anything useful for society as a whole, and don't really need to be optimized overall. Somewhere in between you have stuff like analysis for investing, which is mostly about beating the other guy, but does in some nebulous way apparently help allocate resources more efficiently...
matsemann 5y ago
Jevon's paradox https://en.m.wikipedia.org/wiki/Jevons_paradox https://en.m.wikipedia.org/wiki/Jevons_paradox
- imoverclocked 5y ago
  If this fits here then it maybe it really just means performance matters for adoption?

verdverm 5y ago

Yea, I was thinking you can't really make a generalization like that. 1. Companies may do more with the same amount of CPU, more data, more calculations, despite per evaluation being faster. 2. Improved performance may make usage within reach of more end users 3. Improving widely used packages could reduce, especially those that are used in heavy calculations. With ML, we often see more improvement (reduced learning time) through algorithm research, so library improvements are only part of the story. Of course advances like Capsule Networks and encrypted learning sit in the queue because they are too expensive to compute today, but have great benefit to the outcomes.

bee_rider 5y ago
Also depends on what we're doing. Lots of computational tasks in engineering will take as many flops as you've got and produce a more accurate simulation. The hope would be that the end result would be a somehow better device (better fluid dynamics -> more aerodynamic car). On the other hand, we have things like advertisement which are more of a zero sum competition, don't produce anything useful for society as a whole, and don't really need to be optimized overall. Somewhere in between you have stuff like analysis for investing, which is mostly about beating the other guy, but does in some nebulous way apparently help allocate resources more efficiently...

matsemann 5y ago

Jevon's paradox https://en.m.wikipedia.org/wiki/Jevons_paradox https://en.m.wikipedia.org/wiki/Jevons_paradox

imoverclocked 5y ago
If this fits here then it maybe it really just means performance matters for adoption?