Well here's how I see things going:

Intel and AMD ditch their assembly architectures for Scratch, because drag and drop is very popular lately.

The Boolean is renamed to the biggot by SJW leaders for only supporting binary views.

You must first ask consent to add an item to a linked list, because forcing two items together promotes rape culture.

Apple removes the "h" and "7" keys on all laptop models and gives no reason for their actions.

Linus Torvalds grows an extra middle finger, and it still isn't enough.

Nintendo makes Mario gay and Luigi black to be more inclusive.

LG makes a curved monitor that curves away from you rather than towards you. People buy it in confusion.

Everyone makes the same ad revenue on YouTube, and it is rebranded to OurTube. Luckily, they were able to keep the color scheme.

People finally realize that machine learning is just math, and stop using it everywhere. (Just kidding lol)

AMD and Gucci merge. Nobody understands why.

boolean must be true || false || perhaps || schrödinger

Is your code green?

I've been thinking a lot about this for the past year. There was recently an article on this on slashdot.

I like optimising things to a reasonable degree and avoid bloat. What are some signs of code that isn't green?

* Use of technology that says its fast without real expert review and measurement. Lots of tech out their claims to be fast but actually isn't or is doing so by saturation resources while being inefficient.
* It uses caching. Many might find that counter intuitive. In technology it is surprisingly common to see people scale or cache rather than directly fixing the thing that's watt expensive which is compounded when the cache has weak coverage.
* It uses scaling. Originally scaling was a last resort. The reason is simple, it introduces excessive complexity. Today it's common to see people scale things rather than make them efficient. You end up needing ten instances when a bit of skill could bring you down to one which could scale as well but likely wont need to.
* It uses a non-trivial framework. Frameworks are rarely fast. Most will fall in the range of ten to a thousand times slower in terms of CPU usage. Memory bloat may also force the need for more instances. Frameworks written on already slow high level languages may be especially bad.
* Lacks optimisations for obvious bottlenecks.
* It runs slowly.
* It lacks even basic resource usage measurement.

Unfortunately smells are not enough on their own but are a start. Real measurement and expert review is always the only way to get an idea of if your code is reasonably green.

I find it not uncommon to see things require tens to hundreds to thousands of resources than needed if not more.

In terms of cycles that can be the difference between needing a single core and a thousand cores.

This is common in the industry but it's not because people didn't write everything in assembly. It's usually leaning toward the extreme opposite.

Optimisations are often easy and don't require writing code in binary. In fact the resulting code is often simpler. Excess complexity and inefficient code tend to go hand in hand. Sometimes a code cleaning service is all you need to enhance your green.

I once rewrote a data parsing library that had to parse a hundred MB and was a performance hotspot into C from an interpreted language. I measured it and the results were good. It had been optimised as much as possible in the interpreted version but way still 50 times faster minimum in C.

I recently stumbled upon someone's attempt to do the same and I was able to optimise the interpreted version in five minutes to be twice as fast as the C++ version.

I see opportunity to optimise everywhere in software. A billion KG CO2 could be saved easy if a few green code shops popped up. It's also often a net win. Faster software, lower costs, lower management burden... I'm thinking of starting a consultancy.

The problem is after witnessing the likes of Greta Thunberg then if that's what the next generation has in store then as far as I'm concerned the world can fucking burn and her generation along with it.

In the 90s most people had touched grass, but few touched a computer.

In the 2090s most people will have touched a computer, but not grass.

But at least we'll have fully sentient dildos armed with laser guns to mildly stimulate our mandatory attached cyber-clits, or alternatively annihilate thought criminals.

In other news my prime generator has exhaustively been checked against, all primes from 5 to 1 million. I used miller-rabin with k=40 to confirm the results.

The set the generator creates is the join of the quasi-lucas carmichael numbers, the carmichael numbers, and the primes. So after I generated a number I just had to treat those numbers as 'pollutants' and filter them out, which was dead simple.

Whats left after filtering, is strictly the primes.

I also tested it randomly on 50-55 bit primes, and it always returned true, but that range hasn't been fully tested so far because it takes 9-12 seconds per number at that point.

I was expecting maybe a few failures by my generator. So what I did was I wrote a function, genMillerTest(), and all it does is take some number n, returns the next prime after it (using my functions nextPrime() and isPrime()), and then tests it against miller-rabin. If miller returns false, then I add the result to a list. And then I check *those* results by hand (because miller can occasionally return false positives, though I'm not familiar enough with the math to know how often).

Well, imagine my surprise when I had zero false positives.

Which means either my code is generating the same exact set as miller (under some very large value of n), or the chance of miller (at k=40 tests) returning a false positive is vanishingly small.

My next steps should be to parallelize the checking process, and set up my other desktop to run those tests continuously.
Concurrently I should work on figuring out why my slowest primality tests (theres six of them, though I think I can eliminate two) are so slow and if I can better estimate or derive a pattern that allows faster results by better initialization of the variables used by these tests.

I already wrote some cases to output which tests most frequently succeeded (if any of them pass, then the number isn't prime), and therefore could cut short the primality test of a number. I rewrote the function to put those tests in order from most likely to least likely.

I'm also thinking that there may be some clues for faster computation in other bases, or perhaps in binary, or inspecting the patterns of values in the natural logs of non-primes versus primes. Or even looking into the *execution* time of numbers that successfully pass as prime versus ones that don't. Theres a bevy of possible approaches.

The entire process for the first 1_000_000 numbers, ran 1621.28 seconds, or just shy of a tenth of a second per test but I'm sure thats biased toward the head of the list.

If theres any other approach or ideas I may be overlooking, I wouldn't know where to begin.

Hello to everyone in this platform. I am a college student who wants to become a software developer from the first class of the high school. Unfortunately, in my country it isn't possible that both study to university exam and learn other stuff(Actually you can if you sleep 6 hours and stay on home every time without a social life). Now I'm glad that I have entered one of the best college in my country, but the information I learn in the college is not enough for me. Because of that I am looking for a good algorithms book that teaches the logic of common algorithms(like binary search, DFS, BFS and the things like that). I know I can learn them on the internet ofc, but currently I have to spend a lot of time on computer so I want to a book version of these information. Sorry for this long post. All book recommendations are appreciated :)