HPC Roots Feed Big Data Branches

By Nicole Hemsoth

February 9, 2014

In this segment of our continuing “HPC Lessons for the Wider Enterprise World” series, we’ll take a look at one of the key movements that’s pushed HPC into the mainstream view—big data. Whether or not it’s an overplayed buzzword, the reality is, the phenomenon is driving new awareness of HPC in a growing set of commercial IT circles; pushing traditional HPC vendors into new enterprise territory; and helping the highest ends of both the commercial and research computing find a new golden era of new tools, frameworks and methodologies for tackling demanding data.

According to the most recent IDC figures, 67% of HPC shops say that they perform what can be categorized as big data analysis. These workloads, which the analyst firm dubs, “high performance data analysis” (HPDA) are expected to grow extensively, increasing from $743.8 million in 2012 to almost $1.4 billion in 2017. Additonally, the storage revenue for high performance data analysis on HPC systems will near almost a billion by 2017, IDC says.

IDC defines HPDA as data-intensive simulation and analysis, involving tasks with “sufficient data volumes and algorithmic complexity to require HPC resources.” This can include existing simulation or new analytical methods, and a variety of data types (structured, unstructured, both) or potentially the use of graph analytics or Hadoop frameworks, for example.

These are striking figures in their own right, but let’s consider the reverse of these numbers for a moment. While HPC might be adopting tools and techniques driven from the big data-laden enterprise (nebulous dividing lines exist terminology-wise when HPC/big data are separated into distinct classifications), this series is focused on the lessons about scalability, reliability, efficiency and extensibility that HPC can teach to the big data masses.

In our own informal opinion survey of experts across the HPC spectrum, a resounding majority saw simple parallels between HPC and commercial big data but noted key differences in terms how each camp thinks about hardware and software tools and resources as well as overall workflow. In sum, the HPC leaders we spoke with for the series saw ample opportunities for HPC technologies to filter out—not just in terms of raw technology, but also in the way of processes, methodologies and approaches to addressing large, complex data volumes that require reasonably good performance.

As Bill Kramer, Deputy Project Director for the Blue Waters project at the National Center for Supercomputing Applications (NCSA) echoed, “Today, we see data analysis and data use surpassing much of the performance capability of commodity interconnects and protocols. HPC has dealt with large scale data for many years, and many of the HPC-like technologies, properly adapted, have the potential to enable new and expanded investigations.”

“Some aspects of what we now call big data are certainly novel and innovative, but in many other corners, big data solutions currently are simply re-inventing the wheel—wheels that have been turning in classical HPC for years, if not decades.” Fritz Ferstl, CTO of Univa says. He points to workload management and distributed file systems as prime examples, noting that “even some of the parallel programming paradigms that are being employed in the big data space seem unnecessarily differentiated from what has been evolving and has matured in classical HPC over two decades.”

When we asked Jack Dongarra, Distinguished Professor at the University of Tennessee and lead at Oak Ridge National Lab about what lessons HPC has to offer the world of mainstream big data, he offered an answer as nuanced as both technology areas. He explained that while it is widely recognized that “big data” is has many meanings, this multiplicity of meanings isn’t necessarily a good thing. Part of the problem is that, like familiar alternatives, such as “data intensive,” what counts as big data is relative to other factors, and therefore changes depending on the perspective—processor, memory, bandwidth, storage—from which it is being viewed.

“Straightforward examples of big data applications in this sense are applications that take all of a supercomputer’s memory or more, or that are too complex to process because the relation between computation and data size is non-linear, or that have real-time processing requirements the velocity of which exceeds the I/O bandwidth,” said Dr. Dongarra.

“Generally speaking,” he said, “there are very few large-scale applications of practical importance that are not data intensive when looked at from some relevant point of view. When looking are application in the HPC space, whether the data comes from new instruments, from massive simulations, or from distributed sensors, deliver eye-glazing quantities of data at unprecedented rates. From an applications perspective, however, discussions of big data have greatly increased the prominence of ‘data-driven’ applications (such as data analytics, top-down queries and predictive modeling), where the operations are defined and propelled not only by large data volumes and data streams, but also by the complexity or heterogeneity of the data involved.”

Dongarra says that although researchers have been successful for some time in processing computer-generated, semi-structured data (big simulations) and structured observational data (big instruments), “they are now more eager to take on the challenges of high volumes of unstructured and heterogeneous observational data (text, images, medical records, etc.), which often come in massive piles of small units and are asynchronously generated. So in that way, big data is redefining the HPC application landscape.”

Rob Clyde, CEO of Adaptive Computing, reminds us that “all enterprises, not just Fortune 500 companies, are collecting and storing massive amounts of data, from social media for retailers to multi-dimensional seismic imaging in oil and gas and everything in between. However, the enterprise is struggling to extract better insights and leverage the data to make data-driven decisions. The process is very manual and time consuming with complex dependencies that need to manage multiple applications. The end result is overutilized siloed environments while others lay idle.

To get up to speed, says Clyde, the enterprise can take a play out of the traditional HPC playbook, which has been dealing with big data for a long time. “The requirements are similar to traditional HPC users; however, the players are different and more prolific as HPC hardware becomes more affordable, even for the mid-market.”

His opinions were validated by a recent survey his company produced. According to their findings, which were the collective ideas of over 400 data center managers, administrators and users in a number of verticals, data is primarily being analyzed by home-grown and highly customized applications. The survey also found that 83 percent believe big data analytics are important to their organization or department, but 90 percent would have greater satisfaction from a better analysis process and 84 percent have a manual process to analyze big data.

Based on their own internal survey, which took a look at the big picture across a number of verticals, “the enterprise severely limits its ability to achieve big data insights rapidly and cost-effectively because they do not recognize the differences between traditional IT workloads and big data workloads. Simply put, siloed environments with no workflow automation to process simulations and data analysis fall short in their ability to extract game-changing information from data. In line with our survey findings, we predict that more of the enterprise will adopt HPC to aid their big data efforts.”

Although Clyde and his team at Adaptive are focused on workload automation and large-scale management of workflows, their findings are worth noting as the “siloed environment” problem is dually encountered in both HPC and enterprise settings. While we’ll talk more about this when we move into our software segment of this special series, it’s worth noting that the complexity challenges extend far beyond the diversity and structure of the data—there is still a profound need for users to put the overall workflows into context of goals, current tools and applications, efficiency and beyond. HPC has been able to understand the finer points of doing this at scale, which means their views on adopting workflows to complex environments should not be overlooked by enterprise users seeking to streamline their big data analytics operations.

In essence, much of what Dongarra, Clyde, and others shared for this and other segments of this HPC-to-enterprise series revolves around the topic of workflow.  As Jack Dongarra noted, “In today’s society, the processing of digital information has become such a routine part of life that the general idea of creating digital workflows, in this generic sense, increasingly pervades even discussions of personal productivity in popular media.”

He argues that the concept of workflows will also dominate much of the thinking about cyberinfrastructure for all kinds of research in the era of data-driven science. “The problems inherent in working with data that are streaming out of instruments and simulations at peta- or exabyte rates, or of integrating and analyzing massive, multi-dimensional data sets, are simply too difficult for things to be otherwise. In terms of challenges to workflow, many domain sciences that produce and manage big data share common constraints.”

HPC and large-scale enterprise analyst Dan Olds, with Gabriel Consulting, reiterated some of these ideas, noting that enterprises are “experiencing an unprecedented expansion in the amount to data that’s available to them and potential uses for that data.” Olds says that while sifting through this data will give them insights into their business, along with potential competitive advantage that simply weren’t possible a few years ago, there’s no free lunch – finding the gold nuggets in the data avalanche requires planning, expertise, and investment in the right technologies.

According to Olds, “Business side analysts are going to demand the ability to sort through massive amounts of raw data in order to find, and test, relationships between disparate factors. For example:  How early in autumn will people start thinking about buying winter clothes? Does this vary by location, age, or family size? What’s the best way to get our winter coat-aplooza sale offer in front of the right buyers at the right time? Framing these questions is their job, gathering, storing, and providing the ability to process the data is the job of the data center. Satisfying the analytic demands of the business is causing a lot of sleepless nights for many a data center manager these days.”

“The problems arise from the scale of data and associated compute power needed to process it. Compounding the challenge is the need for speed – enterprise managers need answers to their questions so that they can make quick decisions on pricing, stock levels, and other important issues,” he continued. An answer that comes too late to take advantage of an opportunity is worthless.

The overriding theme in both enterprise and research HPC data analytics environments is to seek the “big fish” in the seas of data. As we take a look in our next segments in this series at enabling tools and approaches, including cloud computing, hardware acceleration, software methods and tools, and other aspects, the wealth of information about how to manage large, complex data from the HPC community will come into greater focus.

The introductory article in this multiple-part series appearing in February can be found here.

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!

Scalable Informatics Ceases Operations

March 23, 2017

On the same day we reported on the uncertain future for HPC compiler company PathScale, we are sad to learn that another HPC vendor, Scalable Informatics, is closing its doors. Read more…

By Tiffany Trader

‘Strategies in Biomedical Data Science’ Advances IT-Research Synergies

March 23, 2017

“Strategies in Biomedical Data Science: Driving Force for Innovation” by Jay A. Etchings is both an introductory text and a field guide for anyone working with biomedical data. Read more…

By Tiffany Trader

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 assets. Read more…

By Tiffany Trader

Google Launches New Machine Learning Journal

March 22, 2017

On Monday, Google announced plans to launch a new peer review journal and “ecosystem” Read more…

By John Russell

HPE Extreme Performance Solutions

HFT Firms Turn to Co-Location to Gain Competitive Advantage

High-frequency trading (HFT) is a high-speed, high-stakes world where every millisecond matters. Finding ways to execute trades faster than the competition translates directly to greater revenue for firms, brokerages, and exchanges. Read more…

Swiss Researchers Peer Inside Chips with Improved X-Ray Imaging

March 22, 2017

Peering inside semiconductor chips using x-ray imaging isn’t new, but the technique hasn’t been especially good or easy to accomplish. Read more…

By John Russell

LANL Simulation Shows Massive Black Holes Break ‘Speed Limit’

March 21, 2017

A new computer simulation based on codes developed at Los Alamos National Laboratory (LANL) is shedding light on how supermassive black holes could have formed in the early universe contrary to most prior models which impose a limit on how fast these massive ‘objects’ can form. Read more…

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. Read more…

By John Russell

Intel Ships Drives Based on 3D XPoint Non-volatile Memory

March 20, 2017

Intel Corp. has begun shipping new storage drives based on its 3D XPoint non-volatile memory technology as it targets data-driven workloads. Intel’s new Optane solid-state drives, designated P4800X, seek to combine the attributes of memory and storage in the same device. Read more…

By George Leopold

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 assets. 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. 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 campaign. 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

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

New Japanese Supercomputing Project Targets Exascale

March 14, 2017

Another Japanese supercomputing project was revealed this week, this one from emerging supercomputer maker, ExaScaler Inc., and Keio University. The partners are working on an original supercomputer design with exascale aspirations. Read more…

By Tiffany Trader

Nvidia Debuts HGX-1 for Cloud; Announces Fujitsu AI Deal

March 9, 2017

On Monday Nvidia announced a major deal with Fujitsu to help build an AI supercomputer for RIKEN using 24 DGX-1 servers. Read more…

By John Russell

HPC4Mfg Advances State-of-the-Art for American Manufacturing

March 9, 2017

Last Friday (March 3, 2017), the High Performance Computing for Manufacturing (HPC4Mfg) program held an industry engagement day workshop in San Diego, bringing together members of the US manufacturing community, national laboratories and universities to discuss the role of high-performance computing as an innovation engine for American manufacturing. Read more…

By Tiffany Trader

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Leading Solution Providers

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

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 campaign. Read more…

By John Russell

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. 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. Read more…

By John Russell

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

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