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!

CMU’s Latest “Card Shark” – Libratus – is Beating the Poker Pros (Again)

January 20, 2017

It’s starting to look like Carnegie Mellon University has a gambling problem – can’t stay away from the poker table. 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

Weekly Twitter Roundup (Jan. 19, 2017)

January 19, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

France’s CEA and Japan’s RIKEN to Partner on ARM and Exascale

January 19, 2017

France’s CEA and Japan’s RIKEN institute announced a multi-faceted five-year collaboration to advance HPC generally and prepare for exascale computing. Among the particulars are efforts to: build out the ARM ecosystem; work on code development and code sharing on the existing and future platforms; share expertise in specific application areas (material and seismic sciences for example); improve techniques for using numerical simulation with big data; and expand HPC workforce training. It seems to be a very full agenda. Read more…

By Nishi Katsuya and John Russell

HPE Extreme Performance Solutions

Remote Visualization: An Integral Technology for Upstream Oil & Gas

As the exploration and production (E&P) of natural resources evolves into an even more complex and vital task, visualization technology has become integral for the upstream oil and gas industry. Read more…

ARM Waving: Attention, Deployments, and Development

January 18, 2017

It’s been a heady two weeks for the ARM HPC advocacy camp. At this week’s Mont-Blanc Project meeting held at the Barcelona Supercomputer Center, Cray announced plans to build an ARM-based supercomputer in the U.K. while Mont-Blanc selected Cavium’s ThunderX2 ARM chip for its third phase of development. Last week, France’s CEA and Japan’s Riken announced a deep collaboration aimed largely at fostering the ARM ecosystem. This activity follows a busy 2016 when SoftBank acquired ARM, OpenHPC announced ARM support, ARM released its SVE spec, Fujistu chose ARM for the post K machine, and ARM acquired HPC tool provider Allinea in December. Read more…

By John Russell

Women Coders from Russia, Italy, and Poland Top Study

January 17, 2017

According to a study posted on HackerRank today the best women coders as judged by performance on HackerRank challenges come from Russia, Italy, and Poland. Read more…

By John Russell

Spurred by Global Ambitions, Inspur in Joint HPC Deal with DDN

January 17, 2017

Inspur, the fast-growth cloud computing and server vendor from China that has several systems on the current Top500 list, and DDN, a leader in high-end storage, have announced a joint sales and marketing agreement to produce solutions based on DDN storage platforms integrated with servers, networking, software and services from Inspur. Read more…

By Doug Black

Weekly Twitter Roundup (Jan. 12, 2017)

January 12, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

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

France’s CEA and Japan’s RIKEN to Partner on ARM and Exascale

January 19, 2017

France’s CEA and Japan’s RIKEN institute announced a multi-faceted five-year collaboration to advance HPC generally and prepare for exascale computing. Among the particulars are efforts to: build out the ARM ecosystem; work on code development and code sharing on the existing and future platforms; share expertise in specific application areas (material and seismic sciences for example); improve techniques for using numerical simulation with big data; and expand HPC workforce training. It seems to be a very full agenda. Read more…

By Nishi Katsuya and John Russell

ARM Waving: Attention, Deployments, and Development

January 18, 2017

It’s been a heady two weeks for the ARM HPC advocacy camp. At this week’s Mont-Blanc Project meeting held at the Barcelona Supercomputer Center, Cray announced plans to build an ARM-based supercomputer in the U.K. while Mont-Blanc selected Cavium’s ThunderX2 ARM chip for its third phase of development. Last week, France’s CEA and Japan’s Riken announced a deep collaboration aimed largely at fostering the ARM ecosystem. This activity follows a busy 2016 when SoftBank acquired ARM, OpenHPC announced ARM support, ARM released its SVE spec, Fujistu chose ARM for the post K machine, and ARM acquired HPC tool provider Allinea in December. Read more…

By John Russell

Spurred by Global Ambitions, Inspur in Joint HPC Deal with DDN

January 17, 2017

Inspur, the fast-growth cloud computing and server vendor from China that has several systems on the current Top500 list, and DDN, a leader in high-end storage, have announced a joint sales and marketing agreement to produce solutions based on DDN storage platforms integrated with servers, networking, software and services from Inspur. Read more…

By Doug Black

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

UberCloud Cites Progress in HPC Cloud Computing

January 10, 2017

200 HPC cloud experiments, 80 case studies, and a ton of hands-on experience gained, that’s the harvest of four years of UberCloud HPC Experiments. Read more…

By Wolfgang Gentzsch and Burak Yenier

A Conversation with Women in HPC Director Toni Collis

January 6, 2017

In this SC16 video interview, HPCwire Managing Editor Tiffany Trader sits down with Toni Collis, the director and founder of the Women in HPC (WHPC) network, to discuss the strides made since the organization’s debut in 2014. Read more…

By Tiffany Trader

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

AWS Beats Azure to K80 General Availability

September 30, 2016

Amazon Web Services has seeded its cloud with Nvidia Tesla K80 GPUs to meet the growing demand for accelerated computing across an increasingly-diverse range of workloads. The P2 instance family is a welcome addition for compute- and data-focused users who were growing frustrated with the performance limitations of Amazon's G2 instances, which are backed by three-year-old Nvidia GRID K520 graphics cards. Read more…

By Tiffany Trader

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. 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

Vectors: How the Old Became New Again in Supercomputing

September 26, 2016

Vector instructions, once a powerful performance innovation of supercomputing in the 1970s and 1980s became an obsolete technology in the 1990s. But like the mythical phoenix bird, vector instructions have arisen from the ashes. Here is the history of a technology that went from new to old then back to new. Read more…

By Lynd Stringer

Container App ‘Singularity’ Eases Scientific Computing

October 20, 2016

HPC container platform Singularity is just six months out from its 1.0 release but already is making inroads across the HPC research landscape. It's in use at Lawrence Berkeley National Laboratory (LBNL), where Singularity founder Gregory Kurtzer has worked in the High Performance Computing Services (HPCS) group for 16 years. Read more…

By Tiffany Trader

Dell EMC Engineers Strategy to Democratize HPC

September 29, 2016

The freshly minted Dell EMC division of Dell Technologies is on a mission to take HPC mainstream with a strategy that hinges on engineered solutions, beginning with a focus on three industry verticals: manufacturing, research and life sciences. "Unlike traditional HPC where everybody bought parts, assembled parts and ran the workloads and did iterative engineering, we want folks to focus on time to innovation and let us worry about the infrastructure," said Jim Ganthier, senior vice president, validated solutions organization at Dell EMC Converged Platforms Solution Division. Read more…

By Tiffany Trader

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

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

Leading Solution Providers

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

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

Beyond von Neumann, Neuromorphic Computing Steadily Advances

March 21, 2016

Neuromorphic computing – brain inspired computing – has long been a tantalizing goal. The human brain does with around 20 watts what supercomputers do with megawatts. And power consumption isn’t the only difference. Fundamentally, brains ‘think differently’ than the von Neumann architecture-based computers. While neuromorphic computing progress has been intriguing, it has still not proven very practical. 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

The Exascale Computing Project Awards $39.8M to 22 Projects

September 7, 2016

The Department of Energy’s Exascale Computing Project (ECP) hit an important milestone today with the announcement of its first round of funding, moving the nation closer to its goal of reaching capable exascale computing by 2023. Read more…

By Tiffany Trader

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…

By Tiffany Trader

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

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