Showing posts with label HPC Control. Show all posts
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.
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."
TAIPEI,
Taiwan, Sept. 21 — TYAN, an industry-leading server platform design
manufacturer and subsidiary of MiTAC Computing Technology Corporation,
announces support and availability of the NVIDIA Tesla P100, P40 and P4
GPU accelerators with the new NVIDIA Pascal architecture. Incorporating
NVIDIA’s state-of-the-art technologies allows TYAN to offer the
exceptional performance and data-intensive applications features to HPC
users.
“Real-time,
intelligent applications are transforming our world, thus our customers need an
efficient compute platform to deliver responsive and cost-effective AI,” said
Danny Hsu, Vice President of MiTAC Computing Technology Corporation’s TYAN
Business Unit. “TYAN is pleased to work with NVIDIA to market FT77C-B7079 and
TA80-B7071 servers with P100, P40 and P4 to market. The TYAN NVIDIA-based
server platforms allow hyper-scale customers to deploy accurate, responsive AI
solutions, and to reduce inference latency up to 45x. The high throughput and
best in class efficiency of Pascal GPUs make it possible to process exploding
volumes of data to offer cost effective, accurate AI applications.”
“The
NVIDIA Pascal architecture is the computing engine for modern data centers.
Powered by Pascal, Tesla GPUs offer massive leaps in performance and efficiency
required by the ever increasing demand of AI applications,” said Roy Kim, Tesla
Product Lead at NVIDIA. “We’re partnering with TYAN to deliver the accelerated
solutions customers need to deploy HPC applications and AI services.”
TYAN
HPC platforms with support for NVIDIA Tesla P100, P40, P4
4U/8
GPGPU FT77C-B7079 – Support up to 2x Intel Xeon E5-2600 v3/v4 (Broadwell-EP)
processors, 24x DDR4 DIMM slots, 1x PCI-E x8 mezzanine slot for high-speed I/O
option, 10x 3.5″/2.5″ hot-swap SATA 6Gb/s HDDs/SSDs, dual-port 10GbE/GbE LOM,
and (2+1) 3,200W redundant power supplies with 80-Plus Platinum rated.
2U/4
GPGPU TA80-B7071 – Support up to 2x Intel Xeon E5-2600 v3/v4 (Broadwell-EP)
processors, 16x DDR4 DIMM slots, 1x PCI-E x8 slot for high-speed I/O option, 8x
2.5″ hot-swap SAS or SATA 6Gb/s plus 2x 2.5″ internal SATA 6Gb/s HDDs/SSDs,
dual-port 10GbE/GbE LOM, and (1+1) 1,600W redundant power supplies with 80-Plus
Platinum rated.
About
TYAN
TYAN,
a leading server brand of MiTAC Computing Technology Corporation under the
MiTAC Holdings Corporation (TSE:3706), designs, manufactures and markets advanced
x86 and x86-64 server/workstation board and system products. The products are
sold to OEMs, VARs, System Integrators and Resellers worldwide for a wide range
of applications. TYAN enable customers to be technology leaders by providing
scalable, highly-integrated and reliable products such as appliances for cloud
service providers (CSP) and high-performance computing and server/workstation
used in CAD, DCC, E&P and HPC markets. For more information, visit MiTAC
Holdings Corporation’s website at http://www.mic-holdings.com or TYAN’s website at http://www.tyan.com
