Watson Takes a Turn on Wall Street

By Michael Feldman

April 7, 2011

In the wake of Watson’s dominant performance on Jeopardy last month, IBM has taken the technology on the road to showcase it to anyone who’ll listen. On Monday, Watson — or rather, its keepers — headlined the opening session of the High Performance Computing Linux Financial Markets Conference in New York City. There was even a Watson demo at the conference, attracting crowds in IBM’s exhibitor booth.

If presenting a research project to a Wall Street crowd seems unusual, keep in mind that IBM does not intend to keep Watson in the lab forever. Commercialization of the technology is clearly in the company’s plan.

Following its Jeopardy win, the supercomputer’s next task will be to apply its analytic smarts to healthcare applications. IBM and Nuance Communications in collaboration with Columbia University Medical Center and the University of Maryland School of Medicine are looking for ways to use Watson to help doctors with patient diagnoses in a real-world medical setting.

Since I was in town for the HPC Wall Street conference, I got the opportunity to chat with two of the IBM’ers that spoke on the topic — Jean Staten Healy, director of the Cross-IBM Linux group, and Edward Epstein, manager of Unstructured Information at IBM Research — and ask them about the how the technology could be applied to financial services.

First though, I wanted to find out more about the Watson design and how it evolved over the three-year project. Since Epstein was one of the primary developers of the Watson software, he was able to give me a rundown on the supercomputer’s path to Jeopardy stardom.

According to him, Watson had a rather unimpressive start. In its first incarnation, it took two hours to spit out the answer to a question (or rather the question to the answer), which obviously wouldn’t do for a prime-time game show. The IBM engineers soon realized they had to do a serious redesign of the 750,000 lines of code if they were ever to be competitive on Jeopardy.

First off, all the data (dictionaries, encyclopedias, historical texts, etc.) had to be placed into RAM. Waiting precious milliseconds for disk reads is a performance killer, so everything got stuffed into memory for lightening-fast access.

But most of the initial effort to boost execution speed involved scaling out the software such that the hundreds of analytics algorithms and natural language processing (NLP) code could be run in parallel. The algorithms were parallelized across the analytics framework — in this case Apache UIMA (Unstructured Information Management Architecture), an open source information management environment that was at the heart of Watson’s software. Also, the search algorithms that looked up data references were distributed across the available cores of the Watson cluster. When the initial scale-out effort was done, there were about 200 Java processes as well as an additional 200 C++ processes running in parallel on Watson’s hardware.

According to Epstein that effort reduced the average answer time to just over 14 seconds. Since, in Jeopardy, you need have the answer in just a few seconds — in most cases just a fraction of a second right after the clue is read — they still needed another four-fold performance boost. Most of that was achieved by precomputing the deep NLP analysis of the pre-canned text and by hammering on every computation outlier. With that accomplished, the average answer time was trimmed to 3.6 seconds — on par with a human Jeopardy champ.

The software development work and the initial sparring matches for Watson were done on an IBM x86 blade cluster, outfitted with Xeon Nehalem CPUs. That system had the ability to store intermediate results, so that during test runs, the software team could execute a partial scenario, and return to it later to run a new calculation based on those intermediates. Also during development, it was important to run thousands of questions simultaneously, rather than a single question for fast real-time execution. So the system was scaled differently than the final Power 750 cluster that was used in the Jeopardy match.
 
The x86 development cluster had much less powerful processors, less memory, and most importantly less memory bandwidth compared to the Power 750 machine. Fundamentally, Watson is a big data app that feeds large amounts of information through a complex framework of analytics software. The fact the this needs to be done interactively puts particular constraints on performance.

According to Epstein, they needed the performance of the Power 750 to be competitive in Jeopardy. Fortunately, porting the software from the x86 blades system to the Power cluster was fairly straightforward, given that the software stack is all based on portable technology (Java, C++, Linux, and UIMA).

A single 750 node has four 8-core 3.5 GHz Power7 CPUs, and the entire system consisted of ninety such nodes, encapsulating 2,880 CPUs and 16 TB of RAM. The peak performance of the Jeopardy system is estimated to be about 80 teraflops.

The Watson software team added a number of Power7 optimizations to bump up the performance a bit more. Most of that involved using NUMA control to pin software processes to specific resources in the machine. “If you’re really trying to get that last edge in performance, then you do things like that,” said Epstein.

The ninety cluster Power7 was probably a bit of overkill for the Jeopardy match. Epstein estimates that CPU utilization was in the neighborhood of 30 percent during the clue processing (So theoretically, Watson could have been playing two additional Jeopardy matches simultaneously.) In any case, it was Epstein’s task to win the match at any cost, CPU utilization be damned. “I had the luxury of having enough hardware to do this job for Jeopardy,” he explained.

So what is Watson doing on Wall Street? IBM might be looking to attract some willing partners for a Watson-style financial analytics project analogous to the aforementioned healthcare research initiative. Big Blue is obviously proud of the technology and believes the system can be applied to all sorts of deep analytics work.

Epstein himself is currently working in the group involved in the healthcare project, but there are a number of individuals who are exploring “other opportunities.” One group is specifically focused on the financial application space.

IBM’s Healy believes a major focus for the technology in the financial arena will involve risk management. The idea is to provide results that will enable investors and money managers to make very fast decisions based on market conditions. Healy said it would not just involve spitting out a single answer like in Jeopardy, but also provide metrics of confidence about that answer, as well as some sort of evidence trail of its analysis.

Healy also suggested the possibility that Watson could serve as a resource for individuals making personal investments decisions. One could envision a sort of “Ask Watson” application that could serve thousands or even millions of investors simultaneously (assuming the machine was scaled appropriately). For this type of work, Watson might have to solicit information from the user based on the specific investment question. In that sense, Watson couldn’t just be an answer machine; it would need some rudimentary conversational skills as well. While Healy concedes the technology is still in the research stage, from her perspective, it has many applications going forward.

I suspect Watson will show up at a lot of conferences this year as IBM tests the waters for the technology. Deep analytics is broadly applicable to many domains and this has all the makings of a high-margin business for IBM. They just need to gather some proof points.

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!

Researchers Scale COSMO Climate Code to 4888 GPUs on Piz Daint

October 17, 2017

Effective global climate simulation, sorely needed to anticipate and cope with global warming, has long been computationally challenging. Two of the major obstacles are the needed resolution and prolonged time to compute Read more…

By John Russell

Student Cluster Competition Coverage New Home

October 16, 2017

Hello computer sports fans! This is the first of many (many!) articles covering the world-wide phenomenon of Student Cluster Competitions. Finally, the Student Cluster Competition coverage has come to its natural home: H Read more…

By Dan Olds

UCSD Web-based Tool Tracking CA Wildfires Generates 1.5M Views

October 16, 2017

Tracking the wildfires raging in northern CA is an unpleasant but necessary part of guiding efforts to fight the fires and safely evacuate affected residents. One such tool – Firemap – is a web-based tool developed b Read more…

By John Russell

HPE Extreme Performance Solutions

Transforming Genomic Analytics with HPC-Accelerated Insights

Advancements in the field of genomics are revolutionizing our understanding of human biology, rapidly accelerating the discovery and treatment of genetic diseases, and dramatically improving human health. Read more…

Exascale Imperative: New Movie from HPE Makes a Compelling Case

October 13, 2017

Why is pursuing exascale computing so important? In a new video – Hewlett Packard Enterprise: Eighteen Zeros – four HPE executives, a prominent national lab HPC researcher, and HPCwire managing editor Tiffany Trader Read more…

By John Russell

Student Cluster Competition Coverage New Home

October 16, 2017

Hello computer sports fans! This is the first of many (many!) articles covering the world-wide phenomenon of Student Cluster Competitions. Finally, the Student Read more…

By Dan Olds

Intel Delivers 17-Qubit Quantum Chip to European Research Partner

October 10, 2017

On Tuesday, Intel delivered a 17-qubit superconducting test chip to research partner QuTech, the quantum research institute of Delft University of Technology (TU Delft) in the Netherlands. The announcement marks a major milestone in the 10-year, $50-million collaborative relationship with TU Delft and TNO, the Dutch Organization for Applied Research, to accelerate advancements in quantum computing. Read more…

By Tiffany Trader

Fujitsu Tapped to Build 37-Petaflops ABCI System for AIST

October 10, 2017

Fujitsu announced today it will build the long-planned AI Bridging Cloud Infrastructure (ABCI) which is set to become the fastest supercomputer system in Japan Read more…

By John Russell

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

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

Intel Debuts Programmable Acceleration Card

October 5, 2017

With a view toward supporting complex, data-intensive applications, such as AI inference, video streaming analytics, database acceleration and genomics, Intel i Read more…

By Doug Black

OLCF’s 200 Petaflops Summit Machine Still Slated for 2018 Start-up

October 3, 2017

The Department of Energy’s planned 200 petaflops Summit computer, which is currently being installed at Oak Ridge Leadership Computing Facility, is on track t Read more…

By John Russell

US Exascale Program – Some Additional Clarity

September 28, 2017

The last time we left the Department of Energy’s exascale computing program in July, things were looking very positive. Both the U.S. House and Senate had pas Read more…

By Alex R. Larzelere

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

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

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

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

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

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

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

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

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

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

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. 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

IBM Advances Web-based Quantum Programming

September 5, 2017

IBM Research is pairing its Jupyter-based Data Science Experience notebook environment with its cloud-based quantum computer, IBM Q, in hopes of encouraging a new class of entrepreneurial user to solve intractable problems that even exceed the capabilities of the best AI systems. Read more…

By Alex Woodie

Intel, NERSC and University Partners Launch New Big Data Center

August 17, 2017

A collaboration between the Department of Energy’s National Energy Research Scientific Computing Center (NERSC), Intel and five Intel Parallel Computing Cente Read more…

By Linda Barney

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