Innovation and Commoditization in High Performance Computing

By Christopher C. Aycock, MS

January 19, 2007

Consumer-product giants like Kraft or Procter & Gamble have to compete with cheap knock-offs under a retailer’s own brand. How they accomplish this is by developing a new product that does a better job of solving a customer’s need. “Better” can include safer, more effective, and easier to use.

That a pioneering work is copied is not lamentable. A dropping price means both greater accessibility and the incentive for greater creativity. Innovation and commoditization are complementary. If the former is a path, then the latter are its endpoints. An existing commodity product sets the starting location for the innovator’s journey that eventually leads to new products to commoditize.

Being Useful

Consider the x86 CPU. While Intel tried to disown its child by working on Itanium, AMD enhanced the x86 with 64-bit extensions, low power consumption, and multi-core architectures. AMD started with an existing commodity product, innovated, and created what has ultimately become a new commodity product.

To be precise, it’s not the x86 CPU that was important, but rather a CPU that can execute x86 instructions quickly. In creating a new instruction set, Intel required their customers to spend the time and money to port legacy systems to Itanium. AMD’s customers, meanwhile, were able to leverage their existing infrastructure more effectively. In essence, AMD followed the mantra of scientific discovery by standing on the shoulders of a giant. Building over an established baseline is a key factor for success.

Corporations often attempt to create an atmosphere of innovation. Common approaches include “skunk works” that bring A-list people together to work with minimal oversight, or “twenty-percent time” in which employees devote one day each week to a pet project. But more technologically advanced does not always mean better. Different does not equal useful. Innovation is only useful if it solves a problem.

HPC customers require a number of fast processors, a solid operating system, and a robust network. Linux Networx gives customers exactly this by integrating commodity components. In a way, they are doing for HPC today what Dell did for the PC twenty years ago. Compare that approach to Cray’s insistence on building their own network, among other components. Even Apple realized the error of their way and are now using the x86 with an open source kernel.

The Software Component

Certainly in the high-end server market open source operating systems are taking the stage. With its significantly lower cost of adoption, Linux has become a central component for many vendors. Compare that adoption trend to Solaris after the dot-com bust; Solaris finally became open source after Sun lost market share.

The interesting thing about open source software is that technically savvy users may also act as contributors, hence the abundance of open source technical computing tools. Challenges within a user’s domain thus subsequently drive the innovation of HPC software. This principle has led to a curious result in that end users are programming with MPI.

MPI was intended for software engineers. Technical computing customers without a background in computer science would be better served with a tool like MATLAB or Mathematica. Indeed, both of these now feature add-ons for parallel computing. Of special interest is interactive parallel computing, such as with Star-P or even Excel Services.

These commercial applications bring back the price issue. Traditional software licenses charge per node or CPU, which makes the applications inaccessible to some users. A better solution, one that is now available for enterprise computing customers, is the “software-as-a-service” paradigm. It seems feasible that ANSYS could make Fluent and LS-DYNA available on demand, in which customers rent time on a centrally managed cluster. (Note that this model is different from “grid computing,” which is a sharing model and usually only encompasses the underlying systems.)

How to Actually Make Money

Given that successful technology leads towards commoditization, the services business model certainly looks like an appealing strategy. IBM derives more than half of its revenue from consulting and related activities. RedHat exists to offer support for open source software. While this model is increasingly common, it is not the only — nor always the best — one available.

Another possible business strategy is to sell the essential component for a commodity product. Mellanox produces silicon for InfiniBand vendors, whereas Microsoft makes an operating system for PC manufacturers. Both companies were able to ensure the importance of their unique platforms by embracing developers and thereby creating an ecosystem of applications. The consequence of legacy leads to a “competitive moat” in which these companies are protected from rivals.

A third approach is to become a standout integrator of commodity components. Rackable Systems was able to grow in the wake of the dot-com bust under this model. The integration community has a number of their own innovations, such as blades, which reduce resource requirements, and the “personal supercomputer,” which is an easy-to-mange cluster-in-a-box.1

The fourth and most generic method is to innovate over the commodity component. For example, network vendors will only stay in business if they acknowledge the ubiquity of Ethernet, Sockets and TCP/IP. The OpenFabrics Alliance pushes iWARP, while Myricom and Quadrics have both released “10G” products. Compare those to the financial disaster from Dolphin’s SCI network.

Similarly, creating co-processors for existing processors can be a workable strategy. ClearSpeed’s CSX600 and IBM’s Cell-BE both compliment existing chips by adding more number-crunching capacity. Likewise, GPUs and FPGAs permit additional capabilities, especially with stream programming and electronic system-level tools respectively.

Instead of creating co-processors, it is possible to enhance the CPU and processor themselves. Recent strategies for this include virtualization extensions and multi-core architectures.2 All of these examples are innovations over the commodity component.

Observe the Old and Ring in the New

Many of the above cited cases were far from state-of-the-art. A successful venture needs to be useful, not just technologically advanced. Introducing anything new to the market carries with it risks, but building on an established base is a sure way to hedge the bets.

After studying Toyota’s processes, Matthew E. May described the carmaker’s mantra as “no best, only better.” By this, he meant that perfection can be pursued but never obtained. Toyota follows a path of gradual improvement to solve a particular need.

HPC vendors must look for opportunities of both innovation and commoditization if for no other reason than that the customer requires such.

1 It seems feasible that technical computing customers will one day do their work interactively on a desktop machine while intense number crunching is offloaded transparently to the department’s personal supercomputer. That, or offloaded to the application vendor’s own cluster for rent under a service agreement. Any entrepreneur looking for an idea may wish to investigate such a scenario.

2 Of course, massively multi-core chips and numerous co-processors lead to the potential for bus saturation. The solution to this is not to have a bus at all, but rather to embrace a direct-connect architecture. That is, to forgo SMP for NUMA. Because ccNUMAs present data-partitioning issues, naive shared-memory programming will prohibit best possible performance. Any student looking for a project may wish to investigate the partitioned global address space model of programming on massively multi-core processors.

—–

Christopher C. Aycock is wrapping up his PhD from Oxford University, where his thesis topic is in communications programming paradigms for high-performance networks. He can be reached via chris@hpcanswers.com.

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!

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Nvidia CEO Predicts AI ‘Cambrian Explosion’

May 25, 2017

The processing power and cloud access to developer tools used to train machine-learning models are making artificial intelligence ubiquitous across computing pl Read more…

By George Leopold

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

IBM, D-Wave Report Quantum Computing Advances

May 18, 2017

IBM said this week it has built and tested a pair of quantum computing processors, including a prototype of a commercial version. That progress follows an an Read more…

By George Leopold

PRACEdays 2017 Wraps Up in Barcelona

May 18, 2017

Barcelona has been absolutely lovely; the weather, the food, the people. I am, sadly, finishing my last day at PRACEdays 2017 with two sessions: an in-depth loo Read more…

By Kim McMahon

US, Europe, Japan Deepen Research Computing Partnership

May 18, 2017

On May 17, 2017, a ceremony was held during the PRACEdays 2017 conference in Barcelona to announce the memorandum of understanding (MOU) between PRACE in Europe Read more…

By Tiffany Trader

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

HPE Launches Servers, Services, and Collaboration at GTC

May 10, 2017

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, a Read more…

By John Russell

IBM PowerAI Tools Aim to Ease Deep Learning Data Prep, Shorten Training 

May 10, 2017

A new set of GPU-powered AI software announced by IBM today brings automation to many of the tedious, time consuming and complex aspects of AI project on-rampin Read more…

By Doug Black

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

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 ne Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Leading Solution Providers

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is 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 w 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 Read more…

By Steve Campbell

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Eng Read more…

By Tiffany Trader

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 Read more…

By John Russell

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, Read more…

By Tiffany Trader

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 networ Read more…

By Tiffany Trader

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of "quantum supremacy," researchers are stretching the limits of today's most advance Read more…

By Tiffany Trader

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