Hello..

More philosophy of where is Love ?

I am a white arab, and i think i am smart since i have also
invented many scalable algorithms and algorithms..

I think i am smart and i say that we are constructing Love,
look at the fact that economies of scale resulted in the most affordable
price of any product for the consumer without the manufacturer having to
sacrifice profits, it is also Love, and this has permitted great things,
since it permits you to become much more powerful and it also permit you
to become rich or immaterially rich, since look also how the price of
accessing internet has gotten very low, this has permitted you to access
youtube and learn from youtube and from internet at a very low price,
and it is a great thing that has happened to humanity, since i think
that internet is by analogy like multitasking and interaction of many
many human brains that resulted or results in something really big and
really great, so internet also contains the smartness of many many
brains and internet contains a lot of data and data of internet is
growing exponentially and it is good for artificial intelligence, and
you have to know how to be selective by efficiently using the right
tools so that to search efficiently internet and so that to learn from
it. But you have also to know more about education and the forms of
human communication, so i I think that our societies are experiencing
many changes, among the many changes is that we are using multimedia
way of communicating like youtube, but if you notice carefully this way
of such youtube way is a form of human communication that looks like an
oral tradition that lacks good and precise thinking and precise writing,
so i think that we have to be careful since i think that we have to
"master" more the way of good and precise thinking
and precise writing so that to adapt correctly and efficiently,
so there is a "distinction" to be made about the form of human
communication, since i think that the form of good and precise thinking
and precise writing by also using hypertext and hyperlinks is the way to
go. So notice below how i am thinking and writing my philosophy with
a much more efficient way of doing since it is technical and it is a
much more efficient and precise way of thinking and writing. This is why
i have also just said the following:

More of my philosophy about the hyperlink and hypertext revolution and
the web..

I think the best things that have happened to internet is
the exponential growth of data and information on internet(and it is
good for artificial intelligence) and the how it is organized
efficiently, i mean this hypertext and hyperlink way of organizing
information on internet is "so" powerful and i think it is what have
revolutionized internet, here is the definition of hyperlink:

"A hyperlink is a word, phrase, or image that you can click on to jump
to a new document or a new section within the current document.
Hyperlinks are found in nearly all Web pages, allowing users to click
their way from page to page."

So this is why you have to be optimistic since i am also organizing my
thoughts of my philosophy and political philosophy more efficiently in
an hyperlink way too, but i will organize them much more efficiently as
a pdf and html book and you will be able to download them from my website.

So i am very optimistic about our world, since our humanity is
progressing in an exponential way, and data and information in internet
is growing in an exponential way, and our era of globalization has been
characterized by the democratization of technology, democratization of
finance, and democratization of information, and economies of scale
resulted in the most affordable price of any product for the consumer
without the manufacturer having to sacrifice profits(and this economies
of scale needs globalization too) etc., so i think our world is becoming
much more powerful and efficient.

More philosophy about Economics and scalability and productivity and
quality..

I invite you to read the following interesting paper because it is
so important:

Tipping the scales in AI: How leaders capture exponential returns

https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/tipping-the-scales-in-ai

I have just read the above paper and i have understood it and i will
talk more about something really important: In economics, economies of
scale resulted in the most affordable price of any product for the
consumer without the manufacturer having to sacrifice profits, and in
Microeconomics there is a so important thing that we call Diminishing
Marginal Returns in Economies of Scale, and it is a so much important
thing, since the most important in it is that you have to know how to
"avoid" this Diminishing Marginal Returns in Economies of Scale, and i
have invented sophisticated tools to do it, also i have invented
many many scalable algorithms and algorithms and i am still inventing
scalable algorithms and algorithms and i am also inventing other
sophisticated software tools and softwares.

You can read more about Diminishing Marginal Returns in Economies of
Scale here:

Economies of Scale

https://courses.lumenlearning.com/suny-microeconomics/chapter/economies-of-scale/

Also there is a so much important thing that we call "scalability",
and in economy you have to know how to "scale" efficiently productivity,
but you have to scale efficiently productivity by avoiding the above
problem of Diminishing Marginal Returns in Economies of Scale, and here
is what i am a saying about the scalability of productivity:

I have also just posted about the following thoughts from the following
PhD computer scientist:

https://lemire.me/blog/about-me/

Read more here his thoughts about productivity:

https://lemire.me/blog/2012/10/15/you-cannot-scale-creativity/

And i think he is making a mistake:

Since we have that Productivity = Output/Input

But better human training and/or better tools and/or better human
smartness and/or better human capacity can make the Parallel
productivity part much bigger that the Serial productivity part, so it
can scale much more (it is like Gustafson's Law).

And it looks like the following:

About parallelism and about Gustafson’s Law..

Gustafson’s Law:

• If you increase the amount of work done by each parallel
task then the serial component will not dominate
• Increase the problem size to maintain scaling
• Can do this by adding extra complexity or increasing the overall
problem size

Scaling is important, as the more a code scales the larger a machine it
can take advantage of:

• can consider weak and strong scaling
• in practice, overheads limit the scalability of real parallel programs
• Amdahl’s law models these in terms of serial and parallel fractions
• larger problems generally scale better: Gustafson’s law

Load balance is also a crucial factor.

So read my following thoughts about the Threadpool to notice that my
Threadpool that scales very well does Load balance well:

About the Threadpool..

I have just read the following:

Concurrency - Throttling Concurrency in the CLR 4.0 ThreadPool

https://docs.microsoft.com/en-us/archive/msdn-magazine/2010/september/concurrency-throttling-concurrency-in-the-clr-4-0-threadpool

But i think that both the methodologies from Microsoft of the Hill
Climbing and of the Control Theory using band pass filter or match
filter and discrete Fourier transform have a weakness, there weakness is
that they are "localized" optimization that maximize the throughput , so
they are not fair, so i don't think i will implement them, so then you
can use my following invention of an efficient Threadpool engine with
priorities that scales very well (and you can use a second Threadpool
for IO etc.):

https://sites.google.com/site/scalable68/an-efficient-threadpool-engine-with-priorities-that-scales-very-well

And here is my other Threadpool engine with priorities:

https://sites.google.com/site/scalable68/threadpool-engine-with-priorities

And read my following previous thoughts to understand more:

About the strategy of "work depth-first; steal breadth-first"..

I have just read the following webpage:

Why Too Many Threads Hurts Performance, and What to do About It

https://www.codeguru.com/cpp/sample_chapter/article.php/c13533/Why-Too-Many-Threads-Hurts-Performance-and-What-to-do-About-It.htm

Also I have just looked at the following interesting video about Go
scheduler and Go concurrency:

Dmitry Vyukov — Go scheduler: Implementing language with lightweight
concurrency

https://www.youtube.com/watch?v=-K11rY57K7k

And i have just read the following webpage about the Threadpool of
microsoft .NET 4.0:

https://blogs.msdn.microsoft.com/jennifer/2009/06/26/work-stealing-in-net-4-0/

And as you are noticing the first web link above is speaking about the
strategy of "work depth-first; steal breadth-first" , but we have to be
more smart because i think that this strategy, that is advantageous for
cache locality, works best for recursive algorithms, because a thread is
taking the first task and after that the algorithm is recursive, so it
will put the childs tasks inside the local work-stealing queue, and the
other threads will start to take from the work-stealing queue, so the
work will be distributed correctly, but as you will notice that this
strategy works best for recursive algorithms, but when you you
iteratively start many tasks, i think we will have much more contention
on the work-stealing queue and this is a weakness of this strategy,
other than that when it is not a recursive algorithm and the threads are
receiving from the global queue so there will be high contention on the
global queue and this is not good. MIT's Cilk and Go scheduler and the
Threadpool of Microsoft and Intel® C++ TBB are using this strategy of
"work depth-first; steal breadth-first". And as you are noticing that
they are giving more preference to cache locality than scalability.