LexisNexis Brings Its Data Management Magic To Bear on Scientific Data

By John E. West

July 23, 2009

LexisNexis has built its business on bringing together billions of different records from many different sources. Its data and tools allow customers to query those data to find the names of everyone who registered a car last week in Miami with a license plate that has an “O” and an “H” in it. Recently the company has been working with Sandia National Laboratories to understand whether the LexisNexis data tools might help researchers manage and understand the flood of data coming from the supercomputers and high resolution scientific instruments that drive discovery today.

LexisNexis specializes in data — lots of data — about you, me, and just about every other person in the US that has any kind of digital fingerprint. These data come from thousands of databases about all kinds of transactions and public records that are kept by companies and agencies around the US. But just having the data isn’t very useful; LexisNexis has to be able to access it on behalf of their customers to help them make complex decisions about what businesses to start or stop, what 500,000 people to send a packet of coupons too, or which John Smith living in California to get a search warrant for.

That infrastructure that LexisNexis uses to do all of this is called the Data Analytics Supercomputer (DAS), and it has been in development and use by the company for a decade supporting its own data services business. The DAS has both hardware and software components, and if you want to host your own internal DAS, it comes as a complete solution ready to run. John Simmons, the CTO for the LexisNexis Special Services Group, explains that while the hardware is based on standard Intel Xeon processors and motherboards from Supermicro, the configuration is specialized to facilitate the rapid processing of very large data streams. So the company specs, configures and assembles the hardware along with the software into a complete system.

A DAS is comprised of some combination of both data refinery and data delivery nodes. These nodes handle the processing of data queries and presentation of results, and up to 500 of them can be connected together by a non-blocking switch (from Force10 Networks, again a standard commercial part) that allows the nodes participating in an operation to cooperate directly with one another. A DAS larger than 500 nodes can be assembled by linking together multiple 500-node sub-assemblies. The system runs a standard Linux kernel with non-essential services turned off to reduce OS jitter and improve performance.

How does it all perform? The company says that in 2008 one of its DAS systems was 14 percent faster than the then TeraSort champion, Hadoop, on a cluster that used less than half of the hardware. Interestingly, LexisNexis also claims that its approach needed 93 percent less code that the Hadoop solution, and this is a big part of the system’s appeal.

LexisNexis achieves such work specification efficiency by using its own Enterprise Control Language (ECL), a declarative language developed specifically by the company to allow non-specialist users to construct queries. LexisNexis productivity studies show that ECL is about five times more efficient than SQL for specifying the same tasks, and Simmons gave me an example of a specific data function that was coded in 590 lines of assembly, 90 lines of C, and just two lines of ECL. When you are building a data query engine that has to be accessible by a community of non-specialists, ease of use matters.

There are other commercial solutions in this area, of course. We’ve written about Pervasive Software’s DataRush framework before, as well as IBM’s System S. But none of those have the maturity or credibility at scale as the LexisNexis solution.

Even the unimaginative can conjure scenarios in which this kind of capability might be of interest to law enforcement and intelligence agencies, but LexisNexis has been trying something new with its big data engine: scientific data analysis. This week the company started talking about a year-long partnership it’s had with Sandia National Laboratories to use the DAS to understand and manage the kinds of very large scientific datasets that high resolution instruments and supercomputers routinely produce.

Richard Murphy of Sandia explained that they have been evaluating how the DAS could fit into the scientific computing workflow. For example, Sandia scientists are experimenting with the DAS in an intermediate step in the workflow to identify regions of interest or high correlation with the occurrence of related phenomena in different datasets. These regions can then be extracted, say for visual analysis, or used as input to different applications in derived computations.

One of the benefits of the DAS that Sandia sees for its users beyond the capability to rapidly process very large datasets is the relative simplicity of the ECL — scientists can stay focused on their domains yet still construct relatively complex queries of their data without a lot of extra cognitive overhead.

The DAS also has potential for Sandia in analyzing the output of large ensembles of simulations — as you might find in climate scenario simulations, for example — all at once, and trying to find features and relationships across the entire ensemble of what could be hundreds of terabytes of output data. Murphy also talked about an application for the validation of scientific codes where the DAS would serve as the engine for comparing computed solutions with data collected from physical experiments. Early results have been promising, and Sandia is making plans for future efforts to take the work further.

Legacy approaches to data management and exploration have begun to sag and split under the strain of soaring data volumes. Mass storage archive systems are capable of preserving petabytes of data but don’t help users find it again. And traditional relational database management systems failed at helping us manage the breadth and complexity of scientific data. The LexisNexis solution, and technologies like it that are being developed to deal with petabyte-scale datasets from first principles, offer a departure from established thinking that may finally give us the tools we need to continue turning all those bits of data we produce and collect into information about the world around us.

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!

HPC-as-a-Service Finds Toehold in Iceland

December 11, 2017

While high-demand workloads (e.g., bitcoin mining) can overheat data center cooling capabilities, at least one data center infrastructure provider has announced an HPC-as-a-service offering that features 100 percent fre Read more…

By Doug Black

HPC Iron, Soft, Data, People – It Takes an Ecosystem!

December 11, 2017

Cutting edge advanced computing hardware (aka big iron) does not stand by itself. These computers are the pinnacle of a myriad of technologies that must be carefully woven together by people to create the computational c Read more…

By Alex R. Larzelere

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit and Sierra. The new AC922 server pairs two Power9 CPUs with f Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Explore the Origins of Space with COSMOS and Memory-Driven Computing

From the formation of black holes to the origins of space, data is the key to unlocking the secrets of the early universe. Read more…

PEZY President Arrested, Charged with Fraud

December 6, 2017

The head of Japanese supercomputing firm PEZY Computing was arrested Tuesday on suspicion of defrauding a government institution of 431 million yen (~$3.8 million). According to reports in the Japanese press, PEZY founde Read more…

By Tiffany Trader

HPC Iron, Soft, Data, People – It Takes an Ecosystem!

December 11, 2017

Cutting edge advanced computing hardware (aka big iron) does not stand by itself. These computers are the pinnacle of a myriad of technologies that must be care Read more…

By Alex R. Larzelere

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Microsoft Spins Cycle Computing into Core Azure Product

December 5, 2017

Last August, cloud giant Microsoft acquired HPC cloud orchestration pioneer Cycle Computing. Since then the focus has been on integrating Cycle’s organization Read more…

By John Russell

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

HPE In-Memory Platform Comes to COSMOS

November 30, 2017

Hewlett Packard Enterprise is on a mission to accelerate space research. In August, it sent the first commercial-off-the-shelf HPC system into space for testing Read more…

By Tiffany Trader

SC17 Cluster Competition: Who Won and Why? Results Analyzed and Over-Analyzed

November 28, 2017

Everyone by now knows that Nanyang Technological University of Singapore (NTU) took home the highest LINPACK Award and the Overall Championship from the recently concluded SC17 Student Cluster Competition. We also already know how the teams did in the Highest LINPACK and Highest HPCG competitions, with Nanyang grabbing bragging rights for both benchmarks. Read more…

By Dan Olds

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

SC Bids Farewell to Denver, Heads to Dallas for 30th Anniversary

November 17, 2017

After a jam-packed four-day expo and intensive six-day technical program, SC17 has wrapped up another successful event that brought together nearly 13,000 visit Read more…

By Tiffany Trader

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

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

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

Leading Solution Providers

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Tensors Come of Age: Why the AI Revolution Will Help HPC

November 13, 2017

Thirty years ago, parallel computing was coming of age. A bitter battle began between stalwart vector computing supporters and advocates of various approaches to parallel computing. IBM skeptic Alan Karp, reacting to announcements of nCUBE’s 1024-microprocessor system and Thinking Machines’ 65,536-element array, made a public $100 wager that no one could get a parallel speedup of over 200 on real HPC workloads. Read more…

By John Gustafson & Lenore Mullin

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

Intel Launches Software Tools to Ease FPGA Programming

September 5, 2017

Field Programmable Gate Arrays (FPGAs) have a reputation for being difficult to program, requiring expertise in specialty languages, like Verilog or VHDL. Easin Read more…

By Tiffany Trader

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

Share This