Showing posts with label High Performance Computing. Show all posts
Google’s Making Its Own Chips Now. Time for Intel to Freak Out
The
Internet’s most powerful company sent a few shock waves through the tech world
yesterday when it revealed that a new custom-designed chip helps run what is
surely the future of its vast online empire: artificial intelligence.
In
building its own chip, Google has taken yet another step along a path that has
already remade the tech industry in enormous ways. Over the past decade, the
company has designed all sorts of new hardware for the massive data centers
that underpin its myriad online services, including computer servers,
networking gear, and more. As it created services of unprecedented scope and
size, it needed a more efficient breed of hardware to run these services. Over
the years, so many other Internet giants have followed suit, forcing a seismic
shift in the worldwide hardware market.
With
its new chip, Google’s aim is the same: unprecedented efficiency. To take AI to
new heights, it needs a chip that can do more in less time while consuming less
power. But the effect of this chip extends well beyond the Google empire. It
threatens the future of commercial chip makers like Intel and
nVidia—particularly when you consider Google’s vision for the future. According
to Urs Hölzle, the man most responsible for the global data center network that
underpins the Google empire, this new custom chip is just the first of many.
No,
Google will not sell its chips to other companies. It won’t directly compete
with Intel or nVidia. But with its massive data centers, Google is by far the
largest potential customer for both of those companies. At the same time, as
more and more businesses adopt the cloud computing services offered by Google,
they’ll be buying fewer and fewer servers (and thus chips) of their own, eating
even further into the chip market.
Indeed,
Google revealed its new chip as a way of promoting the cloud services that let
businesses and coders tap into its AI engines and build them into their own
applications. As Google tries to sell other companies on the power of its AI,
it’s claiming—in rather loud ways—that it boasts the best hardware for running
this AI, hardware that no other company has.
Google’s
Need for Speed
Google’s
new chip is called the Tensor Processing Unit, or TPU. That’s because it helps
run TensorFlow, the software engine that drives the Google’s deep neural
networks, networks of hardware and software that can learn particular tasks by
analyzing vast amounts of data. Other tech giants typically run their deep
neural nets with graphics processing units, or GPUs—chips that were originally
designed to render images for games and other graphics-heavy applications.
These are well-suited to running the types of calculations that drive deep
neural networks. But Google says it has built a chip that’s even more
efficient.
According
to Google, it tailored the TPU specifically to machine learning so that it
needs fewer transistors to run each operation. That means it can squeeze more
operations into the chip with each passing second.
For
now, Google is using both TPUs and GPUs to run its neural nets. Hölzle declined
to go into specifics on how exactly Google was using its TPUs, except to say
that they handle “part of the computation” needed to drive voice recognition on
Android phones. But he said that Google would be releasing a paper describing
the benefits of its chip and that Google will continue to design new chips that
handle machine learning in other ways. Eventually, it seems, this will push
GPUs out of the equation. “They’re already going away a little,” Hölzle says.
“The GPU is too general for machine learning. It wasn’t actually built for
that.”
That’s
not something nVidia wants to hear. As the world’s primary seller of GPUs,
nVidia is now pushing to expand its own business into the AI realm. As Hölzle
points out, the latest nVidia GPU offers a mode specifically for machine
learning. But clearly, Google wants the change to happen faster. Much faster.
The
Smartest Chip
In
the meantime, other companies, most notably Microsoft, are exploring another
breed of chip. The field-programmable gate array, or FPGA, is a chip you can
re-program to perform specific tasks. Microsoft has tested FPGAs with machine
learning, and Intel, seeing where this market was going, recently acquired a
company that sells FPGAs.
Some
analysts think that’s the smarter way to go. An FPGA provides far more
flexibility, says Patrick Moorhead, the president and principal analyst at Moor
Insights and Strategy, a firm that closely follows the chip business. Moorhead
wonders if the new Google TPU is “overkill,” pointing out that such a chip
takes at least six months to build—a long time in the incredibly competitive
marketplace in which the biggest Internet companies compete.
But
Google doesn’t want that flexibility. More than anything, it wants speed. Asked
why Google built its chip from scratch rather than using an FPGA, Hölzle said:
“It’s just much faster.”
Core
Business
Hölzle
also points out that Google’s chip doesn’t replace CPUs, the central processing
units at the heart of every computer server. The search giant still needs these
chips to run the tens of thousands of machines in its data centers, and CPUs
are Intel’s main business. Still, if Google is willing to build its own chips
just for AI, you have to wonder if it would go so far as to design its own CPUs
as well.
Hölzle
plays down the possibility. “You want to solve problems that are not solved,”
he says. In other words, CPUs are a mature technology that pretty much works as
it should. But he also said that Google wants healthy competition in the chip
market. In other words, it wants to buy from many sellers—not just, say, Intel.
After all, more competition means lower prices for Google. As Hölzle explains,
expanding its options is why Google is working with the OpenPower Foundation,
which seeks to offer chip designs that anyone can use and modify.
That’s
a powerful idea, and a potentially powerful threat to the world’s biggest chip
makers. According to Shane Rau, an analyst with research firm IDC, Google buys
about 5 percent of all server CPUs sold on Earth. Over a recent year-long
period, he says, Google bought about 1.2 million chips. And most of those
likely came from Intel. (In 2012, Intel exec Diane Bryant told WIRED that
Google bought more server chips from Intel than all but five other
companies—and those were all companies that sell servers.)
Whatever
its plans for the CPU, Google will continue to explore chips specifically
suited to machine learning. It will be several years before we really know what
works and what doesn’t. After all, neural networks are constantly evolving as
well. “We’re learning all the time,” he says. “It’s not clear to me what the
final answer is.” And as it learns, you can bet that the world’s chip makers
will be watching.
High
Performance Computing is a practice to aggregate computing power that
delivers high performance capabilities in handling large number problems
in science, business or engineering fields.HPC systems involve all
types of servers and micro servers that are being used for highly
computational or data intensive tasks. Currently, as HPC has been firmly
linked to the economic competitiveness and scientific advances it is
becoming important to nations. The worldwide study showcases, 97% of the
companies have adopted supercomputing platforms and says that they
won’t survive without it.
Faster computing capabilities of micro servers or HPC systems,
improved performance efficiency and smarter deployment & management with high
quality of service are some key factors driving the growth of HPC market. The
major challenges for these HPC systems are power, cooling system management and
storage & data management. The importance of storage & data management
would continue to grow in future. In additions to this, software hurdles
continues to grow, which are restraining the growth of HPC market. HPC
technology is being rapidly adopted by the academic institutions and various
industries to build reliable and robust products that would enable to maintain
a competitive edge in the business. Various vendors are also targeting to
provide high performance converged technology solutions. As this trend is
gaining significant relevance, the market is growing steadily and it would
continue its growth in future.
High Performance Computing market analysis by Components
HPC involves various components and some of them could be listed
as Hardware and architecture, software and system management and professional
services. Hardware components are the most essential parts in any HPC system.
The efficiency of the system is totally dependent on the hardware entities in
HPC. Hardware and architecture segment of HPC includes memory capacity
(storage), energy management, servers and network devices. Servers consist of
super computer, divisional, departmental &workgroup. Supercomputers and
departmental units are the fastest elements to be sold in hardware and
architecture section. Another essential component of HPC is software and
management system. It comprises of middleware, programming tools, performance
optimization tools, cluster management and fabric management. Finally,
professional services provided are design & consulting, integration &
deployment and Training & outsourcing.
High Performance Computing market analysis by Deployment
The different types of deployment methods of HPC are Cloud based
and on-premise based methods. Cloud deployment is most popular in the industry,
as cloud-computing technologies are popularly adopted by the players in
different industries. The research shows that cloud technology market is
expected to grow due to its high adoption rate, while the usage on-premise
deployment method would decline slowly.
High Performance Computing market analysis by Application
The major application sections of HPC are High Performance
technical computing and High performance business computing. Technical
computing of the HPC includes various sectors such as Government, Chemicals,
Bio-sciences, Academic institutions, Consumer products, Energy, Electronics and
Others. High performance data analysis is being used in government sector for
national security & crime fighting. In addition to this, HPCs are used in
fraud detection and customer acquisition/retention across other sectors. High
Performance Business Computing includes media entertainment, online gaming,
retail, financial service, ultra scale internet, transportation and others.
High Performance Computing market analysis by Geography
The high performance computing market is being analyzed in
different geographic regions such as North America, Europe, Asia-Pacific and
LAMEA. North America is the largest market for HPC technology due to the
technological advancements and early adoption of technology in the region
followed by Europe.
Competitive Landscape
The key market players are adopting product launch as their
principle strategy to provide high performance solutions in different
industries. Cisco is providing high performance computing solution for
financial services that overcome low latency requirements, high message rate and
throughput requirements, predictability to avoid jitter & spikes and
building large computing grids in cost effective manner.
Some major players in HPC market are IBM, Intel, Fujistu, AMD,
Oracle, Microsoft, HP, Dell, Hitachi Data System and Cisco.
HIGH LEVEL ANALYSIS
Study of the market showcases the current market trends, market
structures, driving factors, limitations and opportunities of the global HPC
market. Porter’s Five Force Model helps in analyzing the market forces,
barriers, strengths, etc., of the global market. Bargaining power of the buyer
is low as the product is highly differentiated and threat of backward
integration is low. The suppliers in this market are more concentrated than
buyers, due to which the bargaining power of suppliers is high. Threat of
substitutes in the global market is high as the switching costs are minimal. As
HPC is a novel concept, threat of new entrants in the industry is high, while,
the moderate number of market players leads to moderate intersegment rivalry in
the market. Value chain analysis helps in analyzing the role of key
stakeholders in the supply chain of the market and would provide new entrants
with knowledge about the value chain of the existing market.
KEY BENEFITS
- Porters five force’s model helps in analyzing the potential of buyers & suppliers, and the competitive sketch of the market, which would guide the market players to develop strategies accordingly
- Assessments are made according to the current business scenario and the future market structure & trends are forecast for the period 2013-2020 by considering 2013 as base year
- The analysis gives a wider view of the global market including its market trends, market structure, limiting factors and opportunities
- The advantages of the market are analyzed to help the stakeholders identify the opportunistic areas in a comprehensive manner
- The value chain analysis provides a systematic study on the key intermediaries involved, which would in turn help the stakeholders in the market to make appropriate strategies
HIGH PERFORMANCE COMPUTING MARKET KEY DELIVERABLES
Access Report @ https://www.wiseguyreports.com/reports/512543-world-high-performance-computing-market-opportunities-and-forecasts-2014-2022
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The CloudLightning Project in Europe has published preliminary results from a survey on Barriers to Using HPC in the Cloud.
"Cloud
computing is transforming the utilization and efficiency of IT infrastructures
across all sectors. Historically, cloud computing has not been used for high
performance computing (HPC) to the same degree as other use cases for a number
of reasons. This executive briefing is a preliminary report of a larger study
on demand-side barriers and drivers of cloud computing adoption for HPC. A more
comprehensive report and analysis will be published later in 2016. From June to
August 2016, the CloudLightning project surveyed over 170 HPC discrete end
users worldwide in the academic, commercial and government sectors on their HPC
use, perceived drivers and barriers to using cloud computing, and uses of cloud
computing for HPC."
As
shown in Figure 2, trust in cloud computing would appear to be a significant
barrier to adopting cloud computing for HPC workloads. Data management concerns
dominate the responses. This is not surprising given the large number of
bio-science and university and academic respondents within the sample. The main
technical barriers relate to communication speeds. This reflects a perceived
lack of cloud infrastructure capable of meeting the communications and I/O
requirements of high-end technical computing. Government policy is again ranked
low it would seem it is neither a driver nor a barrier. Unsurprisingly
availability and capital expenditure are not barriers reflecting their positive
impact on adoption.
According
to the report, there is unlikely to be a full shift of high performance
computing workloads to the cloud in the short term however there is evidence of
demand to meet the capacity limitations of internal infrastructures including
use cases for testing the viability of the cloud or specific software for
various use cases. This is consistent with previous research.
"Funded
by the European Commission’s Horizon 2020 Program for Research and Innovation,
CloudLightning brings together eight project partners from five countries
across Europe. The project proposes to create a new way of provisioning
heterogeneous cloud resources to deliver services, specified by the user, using
a bespoke service description language. Our goal is to address energy
inefficiencies particularly in the use of resources and consequently to deliver
savings to the cloud provider and the cloud consumer in terms of reduced power
consumption and improved service delivery, with hyperscale systems particularly
in mind."
