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More about Energy Consumption and about data centers..

Every 8GB of DDR3 or DDR4 consumes 3 watts of power

A powerful AMD or intel 16 cores CPU consumes around 105 watts of power

An internet modem consumes 10 watts of power

A printer consumes 5 watts of power

A loudspeakers consumes 20 watts of power

A laptop consumes between 50 to 100 watts of power

Computers consume energy more than networks and data centers

36% of energy is consumed by communication networks, 30% by data
centers, and 34% by computers

Computer manufacturing takes about 17% energy, TVs take 11%, smartphones
take 11% and others take 6%

Read more here:

Here’s how much Information Technology is causing Global Energy Consumption

https://www.digitalinformationworld.com/2020/02/the-global-energy-consumption-of-information-technologies-infographic.html

It is related to the following article, i invite you to read it:

Why Energy Is A Big And Rapidly Growing Problem For Data Centers

It’s either a breakthrough in our compute engines, or we need to get
deadly serious about doubling the number of power plants on the planet.

And it is related to my following thoughts, i invite you to read them:

About how to beat Moore’s Law and about Energy efficiency..

I am a white arab and i am also an inventor of many scalable algorithms
and algorithms, and now i will talk about: "How to beat Moore’s Law ?"
and more about: "Energy efficiency"..

How to beat Moore’s Law ?

Here is how, read the following news:

With the following new discovery computers and phones could run
thousands of times faster..

Prof Alan Dalton in the School of Mathematical and Physics Sciences at
the University of Sussex, said:

"We're mechanically creating kinks in a layer of graphene. It's a bit
like nano-origami.

"Using these nanomaterials will make our computer chips smaller and
faster. It is absolutely critical that this happens as computer
manufacturers are now at the limit of what they can do with traditional
semiconducting technology. Ultimately, this will make our computers and
phones thousands of times faster in the future.

"This kind of technology -- "straintronics" using nanomaterials as
opposed to electronics -- allows space for more chips inside any device.
Everything we want to do with computers -- to speed them up -- can be
done by crinkling graphene like this."

Dr Manoj Tripathi, Research Fellow in Nano-structured Materials at the
University of Sussex and lead author on the paper, said:

"Instead of having to add foreign materials into a device, we've shown
we can create structures from graphene and other 2D materials simply by
adding deliberate kinks into the structure. By making this sort of
corrugation we can create a smart electronic component, like a
transistor, or a logic gate."

The development is a greener, more sustainable technology. Because no
additional materials need to be added, and because this process works at
room temperature rather than high temperature, it uses less energy to
create.

Also I think with the following discovery, Graphene can finally be used
in CPUs, and it is a scale out method, read about the following discovery
and you will notice it:

New Graphene Discovery Could Finally Punch the Gas Pedal, Drive Faster CPUs

The scale out method above with Graphene is very interesting, and here
is the other scale up method with multicores and parallelism:

Beating Moore’s Law: Scaling Performance for Another Half-Century

Also read the following:

"Also Modern programing environments contribute to the problem of
software bloat by placing ease of development and portable code above
speed or memory usage. While this is a sound business model in a
commercial environment, it does not make sense where IT resources are
constrained. Languages such as Java, C-Sharp, and Python have opted for
code portability and software development speed above execution speed
and memory usage, while modern data storage and transfer standards such
as XML and JSON place flexibility and readability above efficiency.

The Army can gain significant performance improvements with existing
hardware by treating software and operating system efficiency as a key
performance parameter with measurable criteria for CPU load and memory
footprint. The Army should lead by making software efficiency a priority
for the applications it develops. Capability Maturity Model Integration
(CMMI) version 1.3 for development processes should be adopted across
Army organizations, with automated code analysis and profiling being
integrated into development. Additionally, the Army should shape the
operating system market by leveraging its buying power to demand a
secure, robust, and efficient operating system for devices. These
metrics should be implemented as part of the Common Operating
Environment (COE)."

And about improved Algorithms:

Hardware improvements mean little if software cannot effectively use the
resources available to it. The Army should shape future software
algorithms by funding basic research on improved software algorithms to
meet its specific needs. The Army should also search for new algorithms
and techniques which can be applied to meet specific needs and develop a
learning culture within its software community to disseminate this
information."

Read the following:

https://smallwarsjournal.com/jrnl/art/overcoming-death-moores-law-role-software-advances-and-non-semiconductor-technologies

More about Energy efficiency..

You have to be aware that parallelization of the software
can lower power consumption, and here is the formula
that permits you to calculate the power consumption of
"parallel" software programs:

Power consumption of the total cores = (The number of cores) * (
1/(Parallel speedup))^3) * (Power consumption of the single core).

Also read the following about energy efficiency:

Energy efficiency isn’t just a hardware problem. Your programming
language choices can have serious effects on the efficiency of your
energy consumption. We dive deep into what makes a programming language
energy efficient.

As the researchers discovered, the CPU-based energy consumption always
represents the majority of the energy consumed.

What Pereira et. al. found wasn’t entirely surprising: speed does not
always equate energy efficiency. Compiled languages like C, C++, Rust,
and Ada ranked as some of the most energy efficient languages out there,
and Java and FreePascal are also good at Energy efficiency.

RAM is still expensive and slow, relative to CPUs

And "memory" usage efficiency is important for mobile devices.

So Delphi and FreePascal compilers are also still "useful" for mobile
devices, because Delphi and FreePascal are good if you are considering
time and memory or energy and memory, and the following pascal benchmark
was done with FreePascal, and the benchmark shows that C, Go and Pascal
do rather better if you’re considering languages based on time and
memory or energy and memory.

Read again here to notice it:

https://jaxenter.com/energy-efficient-programming-languages-137264.html

Thank you,
Amine Moulay Ramdane.

Subject	Author
More about Energy Consumption and about data centers..	World90

Faith is under the left nipple. -- Martin Luther

interests / soc.culture.china / More about Energy Consumption and about data centers..

interests / soc.culture.china / More about Energy Consumption and about data centers..