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π-WAM is not adding decimals, it is removing decimals (Was: The invention of vector and matrix registers [NVIDIA Volta])

Cross-posted to 3 groups.

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Hi,

Come on Horsy Boy, you can do better. I
no where wrote something about curve
fitting and/or increasing the precision of

float point numbers:

11.4 Giga Lips with a Budget Laptop
https://github.com/Jean-Luc-Picard-2021/gigabudget

What makes you think LIPS measures precision?
You should know better as a 50% Prologer.

I explictily wrote here what the goal is:

"shave off some of the TOPS to do Prolog inferencing"

What are TOPS? Its a metric for GPUs:

TOPS stands for “Trillions of Operations Per Second.”
https://www.lenovo.com/us/en/glossary/tops-in-computing/

See for yourself what is behind my post:

11.4 Giga Lips with a Budget Laptop
At the end of 2025 we acquired a couple of AI Laptops , that were still 
cheap, since RAM prices had not yet rocketed. The intend was to tap into 
the Copilot+ certified hardware, and shave off some of the TOPS to do 
Prolog inferencing. Amazingly our π-WAM can churn 11.4 GIGA LIPS.

GPUs have evolved form lock-step to independent thread scheduling. This 
made it possible to port the Hack VM variant, that forms the basis for 
our π-WAM, to WebGPU computer shaders. Using NUM_SHADERS = 4096 we could 
produce 11.4 Giga Lips on a Ryzen AI 7 350 w/ Radeon 860M.

See also:

Medium Article - 11.4 Giga Lips
https://medium.com/2989/899b0d5c027b

So just get lost with your crazy irrelevant rant.
When I get more LIPS, things run faster, and
I remove digits from the time dimension.

Got it. Or are you too stupid?

Bye

R Kym Horsell schrieb:
> In comp.lang.prolog Ross Finlayson <ross.a.finlayson@gmail.com> wrote:
> ...
>> Data centers should pay a 10000% excise on electricity,
>> wherever it comes from, a natural regulator of inverted economies.
>> And by ten thousand percent I really mean a ten thousand percent.
> 
> And what would a huge surcharge do?
> Almost always end up affecting the less powerful end of society
> with increased costs to services the AI industry will be doing
> more and more of over time.
> 
> I started a little data center (exaflops.com) many years ago.
> In those distant days people (in fact one was a prof of computer
> science) told me you could never make money running a supercomputer.
> LOL. :)
> 
> I've had many years to watch the trends and a far more efficient
> way to solve resource problems in this area is to change the
> algorithms. There is vast room for improvement, mostly because
> of prevailing attitudes.
> 
> I used to do competetion data science as a sideline. Companies
> would pay almost any price to get an extra decimal place in
> the accuracy of their forecasting processes. But typically
> they were trying to supercharge a system that should be scrapped
> and re-designed from scratch. One area I'm thinking of is
> investment. I had a customer one time -- like many times --
> ask to improve a system that predicted the future price of
> various stocks. The idea (for them) was to have as accurate a
> prediction of what some stock would be worth in a week or a month's
> time so that some moron could use the information to decide when
> to buy or sell the thing.
> 
> I tried to argue the efficient thing was to create a system that
> takes the human out of the loop altogether. It doesnt provide info
> for someone to decide whether or not to follow the advice --
> that is just introducing more noise into the loop and probably
> cancels any benefit of adding a couple decimal places of precision.
> What you *should* do is make a system that is tuned to robustly
> maximize the profit from managing a portfolio.
> 
> Of course they wouldnt come at that. You can't suggest taking the
> managers out of the loop. :)
> 
> Another idea relevant to current AI methods might be to curtail
> use of typical neural net algorithms. Many of them try to squeeze
> the best performance of some NN during the training  phase in
> the hope the resulting system will generalize well enough to be useful
> on new data. But there's kind-of a law that the harder you train
> some system to perform a task well, the less well they can subsuently
> perform a more general version of the same thing. It's amusing when
> you look at the graphs of NN being trained and then tested that
> given a more general problem to solve after being trained to solve
> similar problems very very well the poor old NN does worse that it
> would have done if it had 0 training in the first place.
> 
> It's not like we dont know how to improve this kind of performance.
> Try less hard in the training phase or make it "more noisy".
> Turns out genetic methods are just the ticket for this.
> The training produces less over-fitting and the resulting system
> generalizes better than it did before training and more importantly
> it takes maybe an order of magnitude crunching to produce a good answer
> than the usual over-fit answer.
> 
> Anyway. Have to go and feed the cat.
> 

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