IT as Fuel for the Innovation Engine

By By Ian Foster, Director, Computation Institute at ANL/University of Chicago; Univa Corp.

August 21, 2006

It has been claimed that “IT doesn't matter,” with the implication that IT is now so commoditized that it can no longer be a significant source of competitive advantage.

Conversations with senior executives across many Fortune 2000 companies lead me to disagree with this assessment. True, companies are concerned with controlling IT costs. However, I also find a growing recognition that competitiveness depends on a company's ability to innovate (Steve Jobs says simply: “Innovation distinguishes between a leader and a follower.”). I argue here that there are important strategic opportunities in improving enterprise IT infrastructure to accelerate innovation.

Others have written at length on the importance of innovation, so I will not revisit that topic here. Suffice it to say that in today's increasingly global, hyper-accelerated, and winner-take-all markets, a company's ability to deliver superior products and services before the competition can often make the difference between success and failure. Thus, the ability to innovate consistently and rapidly has emerged as a key differentiator. Successful companies are increasingly focusing attention on the process of innovation and on the empowerment of the knowledge worker — the innovator.

Such competitive pressures are particularly acute in industries where product design and development require the use of sophisticated IT for complex and computationally intensive simulation, design optimization and data analysis (e.g., aerospace and defense, semiconductors and electronics manufacturing, oil and gas, automotive, pharmaceuticals, entertainment and digital media, and financial services, etc.).

In these industries, the knowledge worker needs more than a workstation and an Internet connection; developing a new product or service requires the manipulation and management of large quantities of data, access to large-scale computing and, in many instances, extensive collaboration within distributed teams. The following scenario (based on a real example, but with details changed) introduces key themes:

Strong competitive pressures demand that BestWidget Inc. reduce the time to develop the next version of its best-selling product by half — while also improving quality, reducing manufacturing costs and ensuring adherence to environmental standards. Achieving this goal requires that the design team, spanning five locations across the globe, turn around design revisions four times faster — while also performing an order of magnitude more testing and verification to increase product quality.

While this task is not expressed as an IT challenge, its accomplishment founders on IT issues. Paradoxically, the chief difficulty was not a lack of needed IT capacity and services, but an absence of efficient delivery mechanisms. Dramatic IT improvements over the past decade had given designers their own computers, and each workgroup its own cluster and storage system. These developments had freed designers and workgroups from the limitations of the central mainframe. However, with increasing design complexity, these developments had also become significant barriers to innovation. The ability to manage one's own data had become the burden of managing one's own data (in another industry, chip designers can spend 25 percent of their time managing data!). Convenient access to local data had become inconvenient access to other data — when the data required to complete a design was located at a dozen sites worldwide. The power of a dedicated workstation had become the limited capacity of a single machine — when design goals required thousands of computers. The consequence is that BestWidget designers produced an inferior product, behind schedule and over budget. Not because they are bad designers — on the contrary — but because they just couldn't get access to the data, computing and other resources they needed to do their jobs.

These complaints are all symptoms of the “distributed computing hangover,” a situation where completely decentralized management makes it impractical to allocate resources in alignment with overall objectives. Such difficulties are becoming increasingly widespread and urgent as the importance of continuous, distributed and dynamic innovation grows. However, inherent in such difficulties is also an opportunity for IT to deliver significant competitive advantage.

The key is that knowledge workers should not have to wait for resources or have to adapt their work processes to the peculiarities of available resources. To this end, we must break down barriers that constrain both collaboration among team members and access by team members to needed resources. We must make it possible for innovators to pull needed computing, application and data resources into the innovation process, on a schedule that meets their needs. Furthermore, as multiple design teams are typically active, we must enable the innovative enterprise to balance competing demands for fixed resources, by expressing and enforcing policies that reflect the respective priorities of different design team activities.

In short, we must deliver to innovators:

  • What they need from the IT infrastructure (data, software, computing resources, licenses, etc.) to accomplish necessary tasks.
  • Where they need it (in terms of accessibility to the innovation team), regardless of the location of team members and required resources within or outside the enterprise.
  • When they need it, so the environment is matched to the lifecycle requirements of the innovation cycle.
  • Why they need it, meaning that innovation team activities are consistent with the overall business objectives of the enterprise.

If we think of the innovative enterprise as a high-performance automobile, then our goal in addressing what, where, when and why is to ensure that fuel (computing, data and other resources) is delivered to its engine (the innovators) when needed — not in a best-effort fashion, or after a multi-week manual provisioning process.

In this way, we can ensure that BestWidget designers can access and share data resources quickly, perform computations rapidly, and above all count on the availability of resources as they schedule their work. The company itself can create the highest quality products and services consistent with business priorities and objectives (and given available resources) across all competing tasks.

Enabling this agility requires new capabilities. It requires capacity planning mechanisms for matching supply and demand while taking into account constraints specified as business policies at each level of the infrastructure (ultimately, as in manufacturing supply chains, demand should drive resource planning and scheduling, within policy constraints, to deliver optimal service levels). It requires resource configuration, allocation and scheduling mechanisms to ensure that diverse and distributed assets throughout the enterprise are delivered as and when needed. It also requires monitoring and management mechanisms to track usage, to ensure that demands are met, and to diagnose and correct problems as they occur. Finally, these different mechanisms need to be integrated with enterprise IT infrastructure and tools.

No existing technology addresses all these needs. Product lifecycle management tools address information management requirements, but not the delivery of the computing environments needed to generate or process data. Cluster management tools and workflow tools address elements of workgroup operation and process, but not the larger questions of information delivery and computation scheduling across concurrent activities. Virtualization tools address the configuration of computational environments, but not other aspects of the physical IT infrastructure. Thus, enterprises are left attempting to support the innovation lifecycle by cobbling together disconnected proprietary tools in an ad hoc fashion. The result is non-standard, non-scalable, difficult-to-replicate and difficult-to-manage solutions with limited ability to respond to dynamic business conditions.

Where then should we look for solutions? I believe that Grid technologies have an important role to play. This claim should not be surprising. After all, members of the Grid community have been working for close to a decade on precisely the issues discussed here, with considerable success. For example, the LIGO astronomical observatory delivers 1TB of data a day to eight sites around the world, creating more than 120 million file replicas to date; the U.S. TeraGrid national infrastructure enables flexible, policy-driven access to computing and storage resources at eight science data centers; and the National Cancer Institute's Cancer Bioinformatics Grid provides access to data and services at 60 cancer centers. In each case, Grid technology (specifically, open source Globus software in these examples) is being used to accelerate the pace of innovation.

In the next year or two, I expect that we will see significant progress in the creation and application of IT infrastructures architected specifically to facilitate innovation, and a shift from thinking of IT solely as a cost center to recognizing IT as a value enabler. In the process, we will also see a significant change in how we think about the role of Grid technologies in creating robust, scalable, and adaptive enterprise IT infrastructures.

About Ian Foster

Dr. Ian Foster is associate director of the mathematics and computer science division of Argonne National Laboratory and the Arthur Holly Compton Professor of Computer Science at the University of Chicago. He created the Distributed Systems Lab at both institutions, which has pioneered key Grid concepts, developed Globus software, the most widely deployed Grid software, and led the development of successful Grid applications across the sciences. Foster is also the chief open source strategist and a board member of Univa.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

Nvidia Appoints Andy Grant as EMEA Director of Supercomputing, Higher Education, and AI

March 22, 2024

Nvidia recently appointed Andy Grant as Director, Supercomputing, Higher Education, and AI for Europe, the Middle East, and Africa (EMEA). With over 25 years of high-performance computing (HPC) experience, Grant brings a Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire