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Today's Topics:
1. Clouds .. time to open up the ROI calculator (Stephen Loosley)
2. Carbon-based transistors instead of silicon ? extremely
energy efficient (Stephen Loosley)
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Message: 1
Date: Fri, 06 Sep 2024 15:31:54 +0930
From: Stephen Loosley <[email protected]>
To: "link" <[email protected]>
Subject: [LINK] Clouds .. time to open up the ROI calculator
Message-ID: <[email protected]>
Content-Type: text/plain; charset="UTF-8"
Admins wonder if the cloud was such a good idea after all
As AWS, Microsoft, and Google hike some prices .. time to open up the ROI
calculator
By Richard Speed? Wed 4 Sep 2024 & 128 comment
shttps://http://www.theregister.com/2024/09/04/cloud_buyers_regret/
??
After an initial euphoric rush to the cloud, administrators are questioning the
value and promise of the tech giants' services.
According to a report published by UK cloud outfit Civo, more than a third of
organizations surveyed reckoned that their move to the cloud had failed to live
up to promises of cost-effectiveness. Over half reported a rise in their cloud
bill.
Although the survey, unsurprisingly, paints Civo in a flattering light, some of
its figures may make uncomfortable reading for customers sold on the promises
from hyperscalers.
Like-for-like comparisons for a simple three-node cluster with 200 GB of
persistent storage and a 5 TB data transfer showed prices going from $1,278.58
in 2022 to $1,458.68 in 2024 on Microsoft Azure.
For Google, the price went from $1,107.61 to $1,250.35. According to Civo's
figures, the cost at AWS increased from $1,142.46 to $1,234.59.
"The Kubernetes prices were taken from the hyperscaler pricing calculators," a
Civo spokesperson told The Register.
In the IT world, there is an expectation that bang for buck increases as time
goes by, but in this example, prices are rising faster than the rate of
inflation, and what customers receive for their money remains unchanged.
John David-Lovelock, VP analyst at Gartner, said CIOs had been conditioned not
to expect price increases since the cloud emerged.
"Cost control, based on operating datacenters at massive scale, was part of the
early sales pitch and in the intervening 15 years, it had proven out ? cloud
product costs were stable, and either went down in price or more features were
added at the same price," he told us.
"However, the rapid rise in the cost of electricity post-pandemic, coupled with
the rising cost of skilled IT staff, put cloud delivery under new cost
pressures that had to be passed on, from hyperscalers to platform provider,
from platform provider to software provider, and finally from software
providers to clients.
"While there are cost pressures behind these increases being felt across the
cloud spectrum, opportunistic price increases cannot be ruled out."
Microsoft and Google decided not to officially comment on the survey findings.
However, a representative for one of the hyperscalers retorted that the figures
seemed cherry-picked and pointed out that, as an example, customers using
reserved instances could realize significant savings.
In response to the suggestion that the figures had been "cherry-picked," a Civo
spokesperson said: "The configuration we used ? a three-node cluster with 200
GB Persistent Volume and 5 TB data transfer ? is one we've found to be commonly
selected by our diverse customer base. While we understand that no single setup
can represent every use case perfectly, we believe this configuration offers a
helpful reference point for many potential customers."
An AWS spokesperson sent us a statement: "IT providers often tout their pricing
in direct comparison to AWS, which encourages further price competition. AWS
has reduced prices 134 times since AWS launched in 2006.
"These price reductions have occurred even as AWS has continuously improved
reliability, availability, security, and performance. In addition, AWS offers
management tools that make it easier for customers to monitor and optimize
their cloud costs."
Despite such protestations, analysts have long predicted an increase in public
cloud prices. In 2022, Canalys warned that prices could jump by a third, and
several companies have begun to question the cost of operating services in the
cloud compared to running on-premises.
But is a retreat from the cloud likely? David-Lovelock thinks not: "CIOs cannot
turn their back on cloud."
The giddy enthusiasm might have waned in favor of some hard-nosed ROI
calculations, and some workloads might jump away from cloud vendors, "but this
will not constitute a change in direction ? just a ripple in the stream of
dollars flowing to the cloud."
So, are prices increasing? The answer has to be yes. How much of those rises
are down to the major vendors opportunistically adding of a few percentage
points versus an increase in fixed costs, such as electricity, is pretty much
irrelevant. The advice remains the same: the cloud is here to stay although its
luster has dulled over time.
Time, then, to wheel out the ROI calculator and ensure there's been no stealthy
vendor lock-in.
All clouds and all workloads are, after all, not created equal.
---
------------------------------
Message: 2
Date: Fri, 06 Sep 2024 21:42:02 +0930
From: Stephen Loosley <[email protected]>
To: "link" <[email protected]>
Subject: [LINK] Carbon-based transistors instead of silicon ?
extremely energy efficient
Message-ID: <[email protected]>
Content-Type: text/plain; charset="UTF-8"
Specialist 'carbon nanotube' AI chip built by Chinese scientists is 1st of its
kind and '1,700 times more efficient' than Googles
By Owen Hughes 4th Sep 2024?
https://www.livescience.com/technology/electronics/specialist-carbon-nanotube-ai-chip-built-by-chinese-scientists-is-1st-of-its-kind-and-1700-times-more-efficient-than-googles-version
Scientists in China have developed a tensor processing unit (TPU) that uses
carbon-based transistors instead of silicon ? and they say it's extremely
energy efficient
[Image caption: Unlike conventional TPUs, this new chip is the first to use
carbon nanotubes ? tiny, cylindrical structures made of carbon atoms arranged
in a hexagonal pattern ? in place of traditional semiconductor materials like
silicon. Image credit: Getty Images/sankai]
Scientists in China have built a new type of tensor processing unit (TPU) ? a
special type of computer chip ? using carbon nanotubes instead of a traditional
silicon semiconductor. They say the new chip could open the door to more
energy-efficient artificial intelligence (AI).
AI models are hugely data-intensive and require massive amounts of
computational power to run. This presents a significant obstacle to training
and scaling up machine learning models, particularly as the demand for AI
applications grows.
This is why scientists are working on making new components ? from processors
to computing memory ? that are designed to consume orders of magnitude less
energy while running the necessary computations.
Google scientists created the TPU in 2015 to address this challenge.
These specialized chips act as dedicated hardware accelerators for tensor
operations ? complex mathematical calculations used to train and run AI models.
By offloading these tasks from the central processing unit (CPU) and graphics
processing unit (GPU), TPUs enable AI models to be trained faster and more
efficiently.
Unlike conventional TPUs, however, this new chip is the first to use carbon
nanotubes ?? tiny, cylindrical structures made of carbon atoms arranged in a
hexagonal pattern ? in place of traditional semiconductor materials like
silicon. This structure allows electrons (charged particles) to flow through
them with minimal resistance, making carbon nanotubes excellent conductors of
electricity.
The scientists published their research on July 22 in the journal Nature
Electronics.
https://www.nature.com/articles/s41928-024-01211-2.epdfwwwa
According to the scientists, their TPU consumes just 295 microwatts (?W) of
power (where 1 W is 1,000,000 ?W) and can deliver 1 trillion operations per
watt ? a unit of energy efficiency. By comparison, Google?s Edge TPU can
perform 4 trillion operations per second (TOPS) using 2 W of power.
This makes China?s carbon-based TPU nearly 1,700 times more energy-efficient.
"From ChatGPT to Sora, artificial intelligence is ushering in a new revolution.
But traditional silicon-based semiconductor technology is increasingly unable
to meet the processing needs of massive amounts of data," Zhiyong Zhang,
co-author of the paper and professor of electronics at Beijing?s Peking
University, told TechXplore.
Zhiyong Zhang, "We have found a solution in the face of this global challenge."
The new TPU is composed of 3,000 carbon nanotube transistors and is built with
a systolic array architecture ? a network of processors arranged in a grid-like
pattern.
Systolic arrays pass data through each processor in a synchronized,
step-by-step sequence, similar to items moving along a conveyor belt.
This enables the TPU to perform multiple calculations simultaneously by
coordinating the flow of data and ensuring that each processor works on a small
part of the task at the same time.
This parallel processing enables computations to be performed much more
quickly, which is crucial for AI models processing large amounts of data. It
also reduces how often the memory ? specifically a type called static
random-access memory (SRAM) ? needs to read and write data, Zhang said. By
minimizing these operations, the new TPU can perform calculations faster while
using much less energy.
To test their new chip, the scientists built a five-layer neural network ? a
collection of machine learning algorithms designed to mimic the structure of
the human brain ? and used it for image recognition tasks. The TPU achieved an
accuracy rate of 88% while maintaining power consumption of only 295 ?W.
In the future, similar carbon nanotube-based technology could provide a more
energy-efficient alternative to silicon-based chips, the researchers said.
The scientists plan to continue refining the chip to improve its performance
and make it more scalable, they said, including by exploring how the TPU could
be integrated into silicon CPUs.
--
Owen Hughes is a freelance writer and editor specializing in data and digital
technologies. Previously a senior editor at ZDNET, Owen has been writing about
tech for more than a decade, during which time he has covered everything from
AI, cybersecurity and supercomputers to programming languages and public sector
IT. Owen is particularly interested in the intersection of technology, life and
work ?? in his previous roles at ZDNET and TechRepublic, he wrote extensively
about business leadership, digital transformation and the evolving dynamics of
remote work.
--
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