Convergence: HPC, Big Data & Enterprise Computing

By Gary Johnson

October 28, 2013

Many HPC aficionados probably think of Enterprise Computing as something static and boring: a solved problem; something to be maintained and occasionally updated; or maybe moved to a Cloud – but not a fruitful area for novel approaches or exotic hardware.  Big Data may change those views.  Let’s take a look.

Enterprise Computing

What is Enterprise Computing?  Wiktionary defines enterprise as “A company, business, organization, or other purposeful endeavor.”  In their enterprise computing text, Shan & Earle state that “Enterprise computing involves the development, deployment and maintenance of the information systems required for survival and success in today’s business climate.”

OK, so far so good.  It would seem that enterprise computing is a function of the nature of the purposeful endeavor and the business climate in which it is immersed.

Business Climate Change

The emergence of Big Data is clearly changing the general business climate.  This point was made clearly and compellingly at the recent Big Data’13 conference.  The traditional structured data used in business intelligence, stored in the rows and columns of countless spreadsheets and in SQL databases, is being rapidly augmented by large and rapidly growing volumes of unstructured data.  Google alone is said to be acquiring unstructured data at the rate of a petabyte per hour.  The expectation that competitive advantage can be had by more effectively and more rapidly using all of this structured and unstructured data is now widely held.  This has driven business intelligence into big data analytics – including graph analytics – and more aggressive, complicated and resource-intensive use cases.  These new aspects of business intelligence, in turn, create a demand for new applications, algorithms and computing system architectures.  Starting to sound like our world of HPC?

Big Data is HPC

We have previously observed that Big Data is a form of HPC and should be embraced as such.  This is currently happening in several science disciplines, such as high energy physics, astronomy, and biology.  The need for data analytics and, in particular, visual analytics is driving Big Data-as-HPC into additional sciences – including human health.  The emerging Internet of Things will make Big Data much bigger, more valuable and useful in new ways – further embedding Big Data and data-intensive computing into HPC.

A recent presentation to the Secretary of Energy’s Advisory Board clearly indicates that the advocates of the Department of Energy’s Exascale Initiative understand that the future of high-end computing lies both in Big Compute and Big Data.  So, it’s reasonable to expect that future applications, algorithms and computing architectures for science will be developed to serve both aspects of HPC.

At this point in the development of Big Data, it seems that the emerging solutions are reacting to the fast pace and evolving nature of the data.

Enterprise Data Characteristics

A useful summary of the current state of the general “data deluge” has been provided by Fox, Hey and Trefethen and is drawn upon here.

We distinguish among three different types of data:

  • Observational Data – uncontrolled events happen and we record data about them
    • Examples include: astronomy, earth observation, geophysics, medicine, commerce, business intelligence, social data, the internet of things
    • Experimental Data – we design controlled events for the purpose of recording data about them
      • Examples include: particle physics, photon sources, neutron sources, bioinformatics, engineering design
      • Simulation Data – we create a model, simulate something, and record the resulting data
        • Examples include: weather & climate, nuclear & fusion energy, high-energy physics, materials, chemistry, biology, fluid dynamics, engineering design

Since most data is yet to be collected, we focus here on data rates rather than absolute amounts.  A very high level summary of some of the current or expected data rates in the three data categories is contained in the table below.

Data Type

Data Rate

Timing

Observational
   Astronomy: Square Kilometer Array

>100Tb/sec

2016-2022

   Medicine: Imaging

>1EB/year

now

   Earth Observation

4PB/year

now

   Facebook

>180PB/year

now

Experimental
   Particle Physics: Large Hadron Collider

15PB/year

now

   Photon Sources: Advanced Light Sources

7TB/hour

2015

   Bioinformatics: Human Genome Sequencing

700Pb/year

now

   Bioinformatics: Human Genome Sequencing

10Eb/year

future

Simulation
   Fusion Energy

2PB/time step

now

   Fusion Energy

200PB/time step

2020

   Climate Modeling

400PB/year

now

One immediately notices that the data are hard to compare. The rates for observational data are probably the clearest. For example, if we assume that the Square Kilometer Array were to operate continuously at its full capability, then in the 2022 time frame it would be generating just under 400 exabytes per year. This would appear to make it the world’s largest single data generator – but medical imaging, social data, or the internet of things could well be larger by 2022.

Further note that the business intelligence data used in enterprise computing falls into the category of observational data.  This is probably the most difficult data type to deal with.  Observational data: is collected continuously; comes from a mix of a small number of large sources (e.g. enterprise data collections) and a large number of smaller – but very significant – sources (e.g. medical imaging, social data, internet of things); and its growth rate increases as the capability to collect and resolve such data increases.  So, the associated enterprise computing requirements will be challenging.

Further confirmation of these estimates is provided by the recently launched Chinese Academy of Sciences strategic research project, called NICT (New generation of IC Technology).  It assumes that by 2020 the world will need to utilize zettabytes of data.  Presumably, most of this data will be observational and it will be used by enterprises.

Convergence

So, if Big Data is HPC and if Enterprise Computing is becoming increasingly dependent on Big Data, will this lead to a convergence of Enterprise Computing and HPC?  Judging by the presentations and informal discussions at Big Data’13, such a convergence appears highly likely – if not inevitable – and is, arguably, already underway.

Computing, the internet, social networking, active customer involvement, and the emerging internet of things are causing significant changes in the general business climate and are also creating opportunities for entirely new businesses.  A common element in all of this is data.  It is coming in large volumes and at high rates.  It needs to be analyzed in depth and visualized insightfully to provide useful and actionable business intelligence.  As the demands on such intelligence grow and become more complex, their satisfaction will probably require a mix of compute- and data-intensive techniques.

The demands of Enterprise Computing-as-HPC will surely lead to the development of new and interesting applications and algorithms.  One can easily conceive of such developments as being similar to what we know from the open literature about Big Data applications in the national intelligence community.

This evolved form of Enterprise Computing may also lead to the development of tailored or special purpose computing systems to support unique requirements.  Indeed, the HPC vendor community has already recognized this.  One needs only to look to IBM’s Watson or YarcData’s Urika to see the first steps at a response.

Future Opportunities

Big Data technology is currently a young and fragmented market.  On the software side, data analytics and visualization are rapidly evolving with dozens of current providers and a steady stream of new entrants with novel approaches.  On the hardware side, HPC vendors are adapting or extending current products to the needs of Big Data as well as introducing new products, like Urika and D-Wave‘s quantum computer.

At this point, the data is in the driver’s seat and technologies are reacting to it.  There is a lot more data to come.  Think of the internet of things (and that subset of it called the internet of us).  Speaking of “us”, think of the “crowd” as both a producer and consumer of data – in ever larger quantities and greater varieties.  Think also of other emerging data sources, like 3-D printing which, as it matures, will effectively turn material objects into their representations in data.

We are entering a period during which Big Data will transform Enterprise Computing into a principal venue for creative uses of HPC and the incubation of new businesses.  The convergence has already started.  We in HPC should be full partners in it and help shape the future of Enterprise Computing.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

China Plans 2019 Exascale Machine To Grow Sea Power

August 23, 2017

The glory of having the world's fastest supercomputer, as measured by the Linpack benchmark, has been China's for four years running, first with the 33-petaflops Tianhe-2 and currently with the 93-petaflops TaihuLight. T Read more…

By Tiffany Trader

Microsoft, Intel Unveil FPGA-driven Project Brainwave

August 23, 2017

We know about the seeming light-speed processing power of FPGAs and the natural fit they pose for data-dense AI workloads. But we also know that FPGAs present usability and programmability problems that flummox IT shops. Read more…

By Doug Black

Study Identifies Best Practices for Public-Private HPC Engagement

August 22, 2017

What's the best way for HPC centers in the public sphere to engage with private industry partners to boost the competitiveness of the companies and the larger communities? That question is at the heart of a new study pub Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Leveraging Deep Learning for Fraud Detection

Advancements in computing technologies and the expanding use of e-commerce platforms have dramatically increased the risk of fraud for financial services companies and their customers. Read more…

Google Launches Site to Share its NYC-based Algorithm Research

August 22, 2017

Much of Google’s algorithm development occurs in groups scattered throughout New York City. Yesterday, Google launched a single website - NYC Algorithms and Optimization Team page - to provide a deeper view into all of Read more…

By John Russell

China Plans 2019 Exascale Machine To Grow Sea Power

August 23, 2017

The glory of having the world's fastest supercomputer, as measured by the Linpack benchmark, has been China's for four years running, first with the 33-petaflop Read more…

By Tiffany Trader

Microsoft, Intel Unveil FPGA-driven Project Brainwave

August 23, 2017

We know about the seeming light-speed processing power of FPGAs and the natural fit they pose for data-dense AI workloads. But we also know that FPGAs present u Read more…

By Doug Black

Study Identifies Best Practices for Public-Private HPC Engagement

August 22, 2017

What's the best way for HPC centers in the public sphere to engage with private industry partners to boost the competitiveness of the companies and the larger c Read more…

By Tiffany Trader

Tech Giants Outline Battle Plans for Future HPC Market

August 21, 2017

Four companies engaged in a cage fight for leadership in the emerging HPC market of the 2020s are, despite deep differences in some areas, in violent agreement Read more…

By Doug Black

Microsoft Bolsters Azure With Cloud HPC Deal

August 15, 2017

Microsoft has acquired cloud computing software vendor Cycle Computing in a move designed to bring orchestration tools along with high-end computing access capabilities to the cloud. Terms of the acquisition were not disclosed. Read more…

By George Leopold

HPE Ships Supercomputer to Space Station, Final Destination Mars

August 14, 2017

With a manned mission to Mars on the horizon, the demand for space-based supercomputing is at hand. Today HPE and NASA sent the first off-the-shelf HPC system i Read more…

By Tiffany Trader

AMD EPYC Video Takes Aim at Intel’s Broadwell

August 14, 2017

Let the benchmarking begin. Last week, AMD posted a YouTube video in which one of its EPYC-based systems outperformed a ‘comparable’ Intel Broadwell-based s Read more…

By John Russell

Deep Learning Thrives in Cancer Moonshot

August 8, 2017

The U.S. War on Cancer, certainly a worthy cause, is a collection of programs stretching back more than 40 years and abiding under many banners. The latest is t Read more…

By John Russell

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

Leading Solution Providers

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

IBM Clears Path to 5nm with Silicon Nanosheets

June 5, 2017

Two years since announcing the industry’s first 7nm node test chip, IBM and its research alliance partners GlobalFoundries and Samsung have developed a proces Read more…

By Tiffany Trader

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Singularity HPC Container Technology Moves Out of the Lab

May 4, 2017

Last week, Singularity – the fast-growing HPC container technology whose development has been spearheaded by Gregory Kurtzer at Lawrence Berkeley National Lab Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This