Showing posts with label HPC Hardware. Show all posts
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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When
the movie The Terminator was released in 1984, the notion of computers
becoming self-aware seemed so futuristic that it was almost difficult to
fathom. But just 22 years later, computers are rapidly gaining the
ability to autonomously learn, predict, and adapt through the analysis
of massive datasets. And luckily for us, the result is not a nuclear
holocaust as the movie predicted, but new levels of data-driven
innovation and opportunities for competitive advantage for a variety of
enterprises and industries.
Artificial
intelligence (AI) continues to play an expanding role in the future of
high-performance computing (HPC). As machines increasingly become able to learn
and even reason in ways similar to humans, we’re getting closer to solving the
tremendously complex social problems that have always been beyond the realm of
compute. Deep learning, a branch of machine learning, uses multi-layer
artificial neural networks and data-intensive training techniques to refine
algorithms as they are exposed to more data. This process emulates the
decision-making abilities of the human brain, which until recently was the only
network that could learn and adapt based on prior experiences.
Deep
learning networks have grown so sophisticated they’ve begun to deliver even
better performance than traditional machine learning approaches. One advantage
of deep learning is that there is little need to "train" the system
and define features that might be useful for modeling and prediction. With only
basic labeling, machines can now learn these features independently as more
data is introduced to the model. Deep learning has even begun to surpass the
capabilities and speed of the human brain in many areas, including image,
speech, or text classification, natural language processing, and pattern
recognition.
The
core technologies required for deep learning are very similar to those
necessary for data-intensive computing and HPC applications. Here are a few
technologies that are well-positioned to support deep learning networks.
Multi-core
processors:
Deep
learning applications require substantial amounts of processing power, and a
critical element to the success and usability of deep learning comes with the
ability to reduce execution times. Multi-core processor architectures currently
dominate the TOP500 list of the most powerful supercomputers available today,
with 91% based on Intel processors. Multiple cores can run numerous
instructions at the same time, increasing the overall processing speed for
compute-intensive programs like deep learning, while reducing power
requirements, increasing performance, and allowing for fault tolerance.
The
Intel® Xeon Phi™ Processor, which features a whopping 72 cores, is geared
specifically for high-level HPC and deep learning. These many-core processors
can help data scientists significantly reduce training times and run a wider
variety of workloads, something that is critical to the computing requirements
of deep neural networks.
Software
frameworks and toolkits:
There
are various frameworks, libraries, and tools available today to help software
developers train and deploy deep learning networks, such as Caffe, Theano,
Torch, and the HPE Cognitive Computing Toolkit. Many of these tools are built
as resources for those new to deep learning systems, and aim to make deep
neural networks available to those that might be outside of the machine
learning community. These tools can help data scientists significantly reduce
model training times and accelerate time to value for their new deep learning
applications.
Deep
learning hardware platforms:
Not
every server can efficiently handle the compute-intensive nature of deep
learning environments. Hardware platforms that are purpose-built to handle
these requirements will offer the highest levels of performance and efficiency.
New HPE Apollo systems contain a high ratio of GPUs to CPUs in a dense 4U form
factor, which enables scientists to run deep learning algorithms faster and
more efficiently while controlling costs.
Enabling
technologies for deep learning is ushering in a new era of cognitive computing
that promises to help us solve the world’s greatest challenges with more
efficiency and speed than ever before. As these technologies become faster,
more available, and easier to implement, deep learning technologies will secure
their place in real-world applications – not in science fiction.
Cloud computing is already underway and businesses are swearing by it for better outcome. In fact, there are numbers to back it up too; An Intersect360 Research reveals that the cloud market is supposed to grow at a rate of alteast 10.9% annually in the period 2016-2020, one also needs to know that industry segments like the High Performance Computing (HPC) contributes just 2.4% revenue for cloud computing.
However, in the world of technology, not everybody sees cloud
computing with the proverbial ‘rose-tinted glasses’, like HPC and big data
analytics. Challenges such as security, data control and also the hidden costs
involved in adopting to cloud computing continue to dissuade businesses from
embracing the cloud. Therefore, to say, cloud is the way to go, every CIO
should know certain fundamentals.
1) Data location and control
Mostly when addressed as a technology, people mention that their
data is ‘on cloud’, as if to denote that the cloud is one single place like a
city locality, or a place in one’s home. But that obviously is far from the
truth, as the cloud which is on the internet as a virtual entity, has no
definite location such as a particular server or computer. In such a case
scenario, one needs to analyze, where the data and applications are being
stored, how and when can the content be accessed, who will be controlling the
data flow and information, considering the content is not on premise.
At the same time, questions like how will the availability of
applications be ensured, or how is one to retrieve something back from the
cloud if needed be, and would that be in the same condition as earlier left?
With the Internet of Things (IoT) which is perhaps the biggest subscriber as a
design, to cloud technology, needs to address this question, as location of
data is just about as important, as crucial data for various functions could be
something like a Google server partition, or perhaps some other independent
server closet located somewhere remote.
2) Payment of unused services
Cloud service companies are obviously into the business to make
profit. What this means is, that the deals that they offer, may sometimes
contain unused computing cycles for which the consumer ends up paying for.
Unlike the image of cloud computing like a ‘pay-as-you-go’ buffet,
cloud technology might actually end up being an extra cost, especially if there
are some aspects of the ‘packaged services’ one does not use, nor can it be
transferred to other users who might find utility. So, there needs to be a closer
combing of needs to see the utility served on cloud computing services,
vis-a-vis the costs involved.
3) Off-premise cloud cheaper than on-premise cloud
Another commonly assumed benefit of cloud computing, is that
off-premise cloud could be cheaper than on-premise cloud. To be really making
sense, one obviously needs to look into this issue deeper. When an
organization’s IT needs are relatively stable, it makes little sense to go on a
public or off-premise cloud, as a significant portion of the unused computing
cycle becomes an additional indirect cost paid by organizations.
Also, cloud service providers are considered to be reaping the
benefits of economy of scale, acquiring components and other IT resources on a
large scale, which should translate into cheaper cloud services. However, it
does not turn out true, as any economy or saving achieved from the initial or
preliminary purchases, could then be used up as costs for maintaining those
resources and keeping the operations running.
However, for organizations that have a variable need in cloud
computing and IT infrastructure, it does make sense for them to take up a
‘pay-as-you-go’ model, based on their flexible utility.
Cloud is here to stay
Cloud computing is a technological force, that is here to stay,
and the market figures prove that, especially with the steadily growing
expansion rate. There are organizations who have become more agile and have
indeed benefited on the cost front, by maintaining a cloud based infrastructure
(off-premise), but they are majorly those enterprises whose needs are flexible.
For them, their business scaling and modeling is such, that even the overhead
and maintenance costs added, it works out to be a profitable deal, or perhaps
even adopt the basic ‘pay-as-you-go’ model.
However, there will be market segments where this will never
really apply, as the costs change everything for them. Maintaining a separate
off-premise infrastructure may not be in their best interest, and perhaps a
private on-premise cloud or even a hybrid cloud setup would make sense. To
conclude, it is about picking the adequately matching choice of infrastructure
to the needs of the organization.
