IBM Invents Short-Cut to Assessing Data Quality

By Michael Feldman

February 25, 2010

In what IBM is characterizing as a “breakthrough,” researchers have developed an algorithm that cuts the computational costs of assessing data quality by two orders of magnitude. The idea is to bring uncertainty quantification within reach of present-day supercomputers and even much more computationally-modest machines. The new algorithm has potentially far-reaching applicability, extending to nearly all types of analytics applications as well as scientific modeling and simulation.

The development of the algorithm was performed at IBM Research – Zurich and was presented on Thursday at the Society for Industrial and Applied Mathematics conference in Seattle. The Zurich team has been working on the software for the last year-and-a-half and they were able to patent it at the end of 2009, prior to publishing the results. The announcement this week followed a demonstration on JuGene, the Blue Gene/P system at the Jülich Supercomputing Center in Germany.

In that experiment, 72 Blue Gene racks were used to validate nine terabytes of data in less than 20 minutes. According to IBM researchers, using conventional techniques, that analysis would have consumed more than a day, and in the process, used 100 times as much energy. A sustained performance of 730 teraflops, representing 73 percent of theoretical peak, was demonstrated on the Blue Gene/P machine, and similar or even better efficiencies would be expected on smaller clusters and workstations.

The impetus behind this work is the flood of data that is fed to computers to solve real-world problems — everything from stock portfolio management to computational fluid dynamics. The data can be generated from physical sources, like financial market feeds, weather sensors, electrical grid measurement devices, and Internet streams, as well as from synthetic sources like computer models. “Essentially we live in an ocean of bits and bytes,” says Costas Bekas of IBM Research – Zurich.

The idea, of course, is to employ computers to transform all this raw data into valuable knowledge. But before that, you have to figure out how good the data is, so that the results are trustworthy. And since the collection and generation of all this information is never error-free, one must find a way to quantify all the noise and anomalies in the data.

Statistical techniques to characterize data quality have been around for a while and come under the general term uncertainty quantification, or UQ, for short. There are a number of methods employed for UQ analysis, including the well-known Monte Carlo technique. But one of the most powerful ones uses something called inverse covariance matrix analysis. The problem with this method is that as data sizes grow, the computational cost becomes impractical, even for the most powerful systems. For example, Bekas says a sample of one million data samples would require an exaflop of compute power. That’s roughly 1,000 times the performance of the top petaflop supercomputing systems that exist today. To compensate, people have been manually “remodeling” the data and reducing the size of the problem, but this introduces the element of human bias into the analysis.

The overarching goal of the research was to make UQ practical, not just for elite scientists on supercomputers, but for average users on computing clusters and even personal computers. And because they wanted to cover the whole range of hardware platforms, they needed to design the algorithm so that it would be highly scalable as well as fault tolerant.

The solution the IBM’ers came up with was to replace the inverse covariance matrix method with one using stochastic estimation and iterative refinement. This enabled the researchers to cast the problem as a linear system. “The key is that the number of linear systems that we solve is small,” explains Bekas. “So if you have, say, one million data samples, then you only have to solve 100 linear systems.”

According to Bekas, this model not only enabled them to parallelize the technique, but to reduce the computational cost by a factor of 100. In addition, the algorithm employs a mixed precision scheme such that the main computation can take place in single precision (or even lower), but generate results in double precision (or even higher). While most modern CPUs can’t take advantage of this particular trick, computational accelerators, like Cell processors, GPUs, and presumably even FPGAs, can use this feature to optimal effect.

Fault tolerance is a by-product of the stochastic estimation model. “If for example something goes wrong in your machine while it is solving one of the linear systems, you can safely ignore it and you can go on to the next one,” says Bekas. “On the other hand, if you were to do full matrix inversion [and] something went wrong at the end of a very large matrix calculation, then your data is destroyed.” The technique maintains accuracies of three, four, or even five digits, which according to him, far exceeds what is required for applications.

Now that IBM’s intellectual property related to the algorithm has been patented and the technology is out of the experimental stage, the next step is to begin commercialization. There is no dearth of potential applications: weather forecasting, supply chain management, nuclear weapons simulation, astrophysics, magnetic resonance imaging, and all kinds of business intelligence — essentially any analytics or modeling application where data quality is a driving issue. Perhaps the lowest-hanging fruit is financial portfolio analysis, where exposure to risk is at the very heart of the application. IBM has a Business Analytics and Optimization group within their consulting organization ready to start client engagements.

“You’d be surprised to see how many different disciplines rely on the same basic mathematical problems,” says Bekas. “And this uncertainty quantification is one of them.”

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 (John Wiley & 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 3-D XPoint Non-volatile Memory

March 20, 2017

Intel Corp. has begun shipping new storage drives based on its 3-D XPoint non-volatile memory technology as it targets data-driven workloads. 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

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

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

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

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