IBM Computing on Demand Evolves Toward Cloud Computing Service

By Michael Feldman

August 19, 2009

As IT budgets have gotten squeezed, more customers are looking at cloud computing as a way to avoid up-front capital costs, while getting access to as many CPU cycles as they need. In response, all the big IT firms are scrambling to develop a cloud computing product and services strategy, and IBM is no exception.

IBM has actually enjoyed a bit of head start at this. The company’s Deep Computing on Demand offering was launched back in June 2003, when everyone thought clouds were just fluffy white things in the sky. The original offering allowed HPC customers to rent remote access to supercomputer-type systems maintained by IBM. The initial infrastructure consisted of a Linux cluster of xSeries servers housed at a facility at the company’s Poughkeepsie, New York plant.

One of the first users of the service was GX Technology Corporation, a company that does seismic data imaging for the oil & gas industry. Besides dodging the expense of a cluster build-out, one of the big advantages of the on demand service was that the image processing turnaround was much quicker, since IBM could provision up to a thousand servers at a time, depending upon job size.

In general, the original Deep Computing on Demand service was designed for HPC applications across government, academia and industry. Over the next six years, IBM’s on demand offering evolved into a more general-purpose service, broadening its scope beyond traditional HPC, but keeping its computationally-intensive theme. Today it’s just called Computing on Demand and is run more like a cloud with the ability to create virtual images within individual servers.

David Gelardi, IBM’s vice president of Systems and Technology Group for Worldwide Client Centers, sees their current on demand offering as one of the ways in which a client can take advantage of cloud computing today. “In some sense you could think of Computing on Demand as almost a dress rehearsal for cloud,” he says. “We just didn’t know it.”

Currently, there are six IBM on demand centers strung across the US, Europe and Asia. In most cases customer data is stored locally, so bandwidth and latency dictates that the remote servers not be too remote. Because of that, the centers have tended to migrate to “centers of opportunity.” For example, when the oil & gas industry was booming, IBM maintained a center in Houston. As financial services got hot, they expanded into London and New York. Their newest center is in Japan.

Today the two most active sectors of IBM’s on demand service are the financial services industry and industrial design/automation. In the financial space, the applications that support risk compliance plus the creation and management of new types of financial instruments are the two big drivers right now. In the design space, one of the biggest clients is IBM itself, which periodically rents cycles to do large verification runs on its in-house integrated circuit designs.

The six centers currently house a total of 13,000 processors and 54 terabytes of storage. Customers are offered a choice of hardware: IBM Power CPUs (System p servers), or x86 CPUs (BladeCenter and System x servers) using either Intel Xeon and AMD Opteron processors. On the x86 side, both Linux and Microsoft Windows is supported, while the System p users get their choice of Linux or AIX. IBM-built management software, like the xCAT (Extreme Cluster Administration Toolkit), is layered on top for extra functionality.

At one time, IBM offered remote access to Blue Gene technology, but that’s no longer the case. Gelardi says they couldn’t find a broad enough market for the type of specialized technology and support inherent in a rent-a-Blue-Gene offering. The same goes for the Cell processor. He does, however, see the possibility of incorporating IBM mainframes into the on demand model since these represent fairly dear cycles when customers are getting ready to deploy a mainframe application into production.

As far as pricing goes, there are a number of factors that determine cost, including service commitment, technology requirements, and number of compute cycles. It’s actually quite similar to renting other types of infrastructure, like hotel rooms or cars. If you rent for a day, you get one price, for a week, you get a better deal, and so on. Similarly, you get charged a premium if you rent the compute equivalent of a Ferrari versus a Ford. Customer flexibility related to the Service Level Agreement (SLA) is also a consideration. For example, if a customer needs a 24/7 uptime, that’s going to drive the price up since spare servers have to be set aside to account for the inevitable hardware failures.

Gelardi noted that the $1 per CPU-hour for Sun Microsystems’ now defunct Network.com utility computing service might have sounded good, but was an unworkable business model. At some level, he probably wishes the Sun model would have succeeded since it would have kept prices up for all the players. “If I could get a dollar per CPU-hour, I could pave the roads with gold bullion,” he jokes.

Although the IBM compute service has grown beyond its rent-a-supercomputer roots, it still represents a fairly typical compute utility service. The plan, though, is to evolve into a more complex model, where customers will be offered four different types of cloud infrastructure: compute clouds, development clouds, test clouds, and storage clouds. The current offering will naturally evolve into the compute cloud, but IBM’s intention is to develop purpose-built infrastructure aimed at the other three functions.

IBM is already working on a proof-of-concept project with a large financial institution that is looking to give up to 10,000 programmers the ability to independently develop a database plus application service engine in the cloud. The idea for the developer is to be able to attach their workstation to a virtual machine that represents a much larger system. They will also have the ability to do a refresh, which resets the virtual machine back to its initial state.

Although IBM doesn’t supply hard numbers about the size of its computing on demand business, Gelardi says they have hundreds of clients that are currently active or have been active through the course of the program. When it started out in 2003, he says the service was generating revenue on the order of millions of dollars per year. At this point, he says, that has risen to tens of millions of dollars. “As we start to bring in the other types of clouds — the test clouds, development clouds, storage clouds — we’ll blow through the next level very quickly.”

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 Career Notes (March 2017)

March 1, 2017

In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the high performance computing community. Read more…

By Thomas Ayres

Intel Sets High Bar with Workforce Diversity Program Results

February 28, 2017

Intel’s impressive efforts to achieve workforce diversity and compensation equality edged up yet another notch last year according to the company’s 2016 Diversity and Inclusion Report released today. Read more…

By John Russell

Battle Brews over Trump Intentions for Funding Science

February 27, 2017

The battle over science funding – how much and for what kinds of science – Read more…

By John Russell

Google Gets First Dibs on New Skylake Chips

February 27, 2017

As part of an ongoing effort to differentiate its public cloud services, Google made good this week on its intention to bring custom Xeon Skylake chips from Intel Corp. Read more…

By George Leopold

HPE Extreme Performance Solutions

Manufacturers Reaping the Benefits of Remote Visualization

Today’s manufacturers are operating in an ever-changing atmosphere, and finding new ways to boost productivity has never been more vital.

This is why manufacturers are ramping up their investments in high performance computing (HPC), a trend which has helped give rise to the “connected factory” and Industrial Internet of Things (IIoT) concepts that are proliferating throughout the industry today. Read more…

Thomas Sterling on CREST and Academia’s Role in HPC Research

February 27, 2017

The US advances in high performance computing over many decades have been a product of the combined engagement of research centers in industry, government labs, and academia. Read more…

By Thomas Sterling, Indiana University

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

Weekly Twitter Roundup (Feb. 23, 2017)

February 23, 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

HPE Server Shows Low Latency on STAC-N1 Test

February 22, 2017

The performance of trade and match servers can be a critical differentiator for financial trading houses. Read more…

By John Russell

Thomas Sterling on CREST and Academia’s Role in HPC Research

February 27, 2017

The US advances in high performance computing over many decades have been a product of the combined engagement of research centers in industry, government labs, and academia. Read more…

By Thomas Sterling, Indiana University

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

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

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

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

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

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

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

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

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

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

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

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

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

Leading Solution Providers

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

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

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

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

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

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

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

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