Wolfram Alpha: A Web-Based Application That Embraced Supercomputers

By Michael Feldman

July 9, 2009

It was less than two months ago that Wolfram Alpha launched and introduced the idea of a Web site for universal computation. Wolfram Alpha is based on Wolfram Research’s Mathematica, but uses it to drive a general-purpose computational engine that can be applied across more than a thousand knowledge domains.

The launch created quite a bit of fanfare in the media since Wolfram Alpha was seen (incorrectly) as a rival to search engines like Google and Yahoo. The new application also encapsulated the notion of the Semantic Web, which many envision as “the next big thing.” Add to that the fact that the Web site was built on top of supercomputers and you have all the ingredients for a juicy high-tech story for the masses.

The supercomputer infrastructure was one of the least talked about aspects of the project, but to our publication, one of the most interesting. Schoeller Porter, who now does business development for Wolfram Alpha, wrote about the pre-launch of the Web site in a recent blog post, and how the project outgrew the initial infrastructure plan even before it booted up.

According to Porter, whom I spoke with shortly after he wrote the blog entry, the project’s initial plan devised in February was to roll out Wolfram Alpha on a much smaller scale. The idea was they would make a discrete announcement in the Mathematica community, and users would trickle in. They were anticipating early traffic would be around 200 queries per second. For that kind of computing load, they would be able to get by with a few datacenters populated by modest-sized Web-style clusters — “normal servers you can buy off the shelf from anywhere,” said Porter.

Then in early March, Stephen Wolfram wrote a blog post announcing Wolfram Alpha and they started getting a lot more inquiries about the it. “It clearly hit a nerve in the Semantic Web community,” explained Porter. From that point on, they noticed that every time Wolfram gave a speech on the subject, it got more and more press coverage. They soon realized their backroom project was going to get a great deal more attention than they had originally thought. Now they were anticipating that the initial launch would attract something in the neighborhood of 2,000 queries per second — ten times the original estimate.

As a result, they were forced to scale out the Wolfram Alpha infrastructure. (And thanks to the deep pockets of Wolfram Research, they could do so.) But the time scale was compressed. It was already March and they were looking to launch the site in May. They determined the only way to ramp up the capacity so quickly was to deploy large ready-made clusters, i.e., HPC machines. That’s basically why the 576-node cluster from R Systems (R Smarr) and a slightly smaller Dell HPC cluster were added. The other three datacenters consist of much smaller cluster systems using vanilla servers.

According to Porter, strictly speaking they don’t depend upon supercomputers for the Wolfram Alpha application. The queries are being handled in parallel, but a tightly-coupled system is not required for that. There’s no MPI programming involved. Since Mathematica is the computational engine, the calculations themselves are single threaded, even presumably for operations like matrix multiplication. Aggregating all the queries is where the parallelism comes in, just like any typical Web application.

However, since Wolfram Alpha is all about computation, the extra CPU horsepower and memory performance of HPC servers do not go to waste. Traditional search applications are pretty easy on the CPU, since basically they’re just scanning through an index of Web pages. Wolfram Alpha, on the other hand, is doing heavy-duty math, so there is a much greater use of floating point and high precision fixed-point arithmetic. And all the calculations are being done in real time. “Every time you go the Web site and provide an input, the result you get back is generated on the fly,” explained Porter.

As you might suspect, computational capacity per query is not unlimited. The software automatically times out if a calculation is hogging the CPU. Thus, for example, the Haferman carpet fractal can be run with an iteration of six, but it quits if the iteration is seven or greater. Similarly, if you try to compute the factorial of 250,000 or greater — no dice.

But the Web site’s biggest stress test is probably ahead of it. The May launch of Wolfram Alpha came just as many universities and high schools were shutting down for the year. Since Wolfram Alpha is ideally suited for students and teachers, especially for math and science course work, it wouldn’t be surprising to see a significant uptick in Web site traffic when schools come back into session at the end of August and beginning of September.

According to Porter they expect to expand the infrastructure within the year beyond the 10,000 or so CPU cores they now have deployed. “I expect as we grow, we’ll grow at this supercomputer-sized scale,” he said. The project team is also reevaluating the infrastructure design to determine if they can improve the system as it scales out. In particular, they’re looking at increasing the number of connections from the databases to the compute nodes to maximize throughput.

Since Mathematica currently runs only on commodity processors, for the time being Wolfram Alpha infrastructure will be based on x86 servers. However, Wolfram Research is investigating GPUs and other types of computational accelerators and as support for those technologies are integrated into Mathematica, they will migrate into Wolfram Alpha as well. “But the fundamental limitation isn’t the technology itself,” explained Porter. “It’s how do we enable ordinary folks to be able to take advantage of that technology. I think in some ways Wolfram Alpha is the model to accomplish that.”

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!

Data Vortex Users Contemplate the Future of Supercomputing

October 19, 2017

Last month (Sept. 11-12), HPC networking company Data Vortex held its inaugural users group at Pacific Northwest National Laboratory (PNNL) bringing together about 30 participants from industry, government and academia t Read more…

By Tiffany Trader

AI Self-Training Goes Forward at Google DeepMind

October 19, 2017

DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…

By Doug Black

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

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…

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

Data Vortex Users Contemplate the Future of Supercomputing

October 19, 2017

Last month (Sept. 11-12), HPC networking company Data Vortex held its inaugural users group at Pacific Northwest National Laboratory (PNNL) bringing together ab Read more…

By Tiffany Trader

AI Self-Training Goes Forward at Google DeepMind

October 19, 2017

DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…

By Doug Black

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

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

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

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