Roll up your blue sleeves and get to work.
From an industrial perspective, HPC seems to be a "look, but don't touch" technology. While there is an acknowledged need for HPC by many industrial sectors, the HPC market has traditionally focused on the grand challenge or the "heroic" computing needs of the National Labs and Computing centers. Stan Ahalt, Executive Director of The Ohio Supercomputer Center, and Kathryn L. Kelley believe a focus on Blue Collar™ Computing can help revitalize industrial innovation and usher in a new era of "high touch" HPC.
Commercial forces influence all technologies. The HPC market, which has gone through many changes in recent years, is no exception. Indeed, the first do-it-yourself white box clusters look quite a bit different than the current blade systems available today. And yet, by all accounts, cluster technology is still in its infancy. Understanding the challenges that lay ahead is critical if the full potential of the market can be realized. Fortunately, there have been similar evolutionary technologies in the past from which we may learn some valuable lessons.
For example, let's look at the onset of the commercially viable automobile. Early models were as varied as their creators visions. In 1900 wealthy people bought cars for pleasure, comfort, and status. Rural Americans liked cars because they could cover long distances without depending on trains. One example of vehicular pulchritude, the Duesenberg, was considered one of best, most-expensive American cars made in the early 1900s. The "Duesy" sold for $15,000 at a time when a Ford cost $500 and the Auburn Automobile Company produced around 1,000 of these automobiles. Likewise, the 1921 Winton was a low-silhouette luxury car, costing more than $4,000; only 325 were built that year.
The Ford Model T, made between 1908 and 1927, cost less than most models of the time but was sturdy and practical. The Model T looked like an expensive car but actually was very simply equipped. And more Model Ts were sold than any other type of car at the time -- over 15 million. Farmers, factory workers, schoolteachers, and many other Americans changed from horses or trains to cars when they bought Model Ts.
So the early automobile market was heavily skewed to the low end of the market. Many, many inexpensive models were sold, but relatively few more expensive automobiles were sold, regardless of their capability or appeal.
Compare that early automobile market to today's market. While there are a number of bare-budget cars on the market, the biggest selling car models are those that are available in the mid-range of capability, power, and options. And on the far end of the scale, relatively few models are available to satisfy the discerning automobile customer looking for finely tuned, high-end sports and luxury vehicles. Moreover, the number of high-end autos sold is minuscule compared to the large number of mid-range cars that are sold.
As the automobile market matured, factors developed that increased demand from a full spectrum of the buying public, creating a bell curve of price and performance -- most automobiles that are sold today are mid-priced, and have mid-level performance characteristics. Later, we'll argue that similar demands may cause the high performance computing (HPC) market to mature in a similar fashion.
HPC Commercialization TodayWhat was considered high end computing yesterday has quickly become part of everyday life. Who could have guessed 50 years ago that computing would be used as ubiquitously as pencils? While we are all familiar with the use of computers in most business offices, other uses of computers were unimaginable at the time computers first became commodity items. For example, some dentists have equipped their offices with the latest computer visualization technology so that they can take a 3-D visual image of your tooth. The image of your tooth is sent to an office sculpting machine no bigger than a laser printer. This machine can shape a new crown for your tooth in six to 20 minutes, and the dentist can fit the crown in your mouth immediately. This capability is an example of profoundly powerful computing affecting the fabric of everyday life. Additional examples abound -- human genomics, climate modeling, and jet propulsion, to name a few, have changed the way that science impacts our lives, the environment, and our economies -- and each is based on computation and computational models.
However, the benefits reaped from HPC research and its resulting applications have not transferred to some industries that desperately need an infusion of computation. On the contrary, computational technology has been viewed by some to be at least partially to blame for massive workforce reductions. News sources and economists differ on how many jobs have been displaced due to outsourcing and improved technologies -- both of which allow companies to improve productivity by either employing fewer employees or by employing a less expensive workforce that, in most cases, computes and communicates in a different country.
|Sidebar One: Problems with Building the Industrial HPC Market|
One of the industrial sectors that has been most profoundly effected by these trends has been manufacturing. Nationally, the U.S. has lost almost three million jobs in manufacturing; the states with the most loses include California, Texas, and Ohio. Rarely discussed is how manufacturing might use HPC to improve workforce productivity and manufacturing technologies in order to produce radically improved products and processes. For instance, research involving the development of advanced metals that are only nanometers thick, yet stronger than their thicker counterparts, hold great promise in manufacturing. According to analysts, the market is ripe for higher-end manufacturing and industrial engineering and they expect ...manufacturing will grow faster than the overall economy.
Fortunately, a growing number of companies are beginning to consider the proposition that HPC may be a key tool in increasing competitiveness and improving business. A July 2004 white paper commissioned by the Council on Competitiveness (CoC) and conducted by the International Data Corporation (IDC) surveyed 33 chief technology and information officers from aerospace, automotive, life science, electronics, pharmaceutical, and software companies, to determine the HPC needs of U.S. industry. The survey results are compelling:
- Over 70% indicated their companies could not function without HPC
- Over 25% of companies could quantify HPCâs ROI to their business, whether it saved millions of dollars, shortened production development cycles, or provided faster product-to-market timing (over 50% of affirmative responses).
Thus, while we can already see an emerging industry for HPC applications and HPC software that supports industrial and engineering work, there are cautionary notes as well. There are a number of interesting barriers that must be addressed before HPC is widely viewed as an essential component of our economy.
Industrial Barriers to Entry
According to the CoC study, business demand for HPC is still a relatively underdeveloped market. Over 65% of the reporting companies have important, but currently unsolved computational problems; the rest (35%) need faster computers for their problems. The need for HPC is obvious. Let's discuss some of the traditional and nontraditional barriers that prevent industry from fully utilizing HPC.
- One of the traditional barriers to industry HPC use has been the lack of technical expertise from the existing and emerging workforce. According to the CoC study, company technology officers cite a lack of trained computational scientists to apply HPC to company's problems. Many computer science graduates do not take an extensive number of science and engineering courses; likewise, engineering and science graduates are not traditionally trained in computational methods and they have only been marginally represented in HPC. Scientists and engineers learn domain applications, while CS students learn programming skills, however, both sets of skills are needed in many industries. Put simply, engineers need to know how to write software, and computer scientists need to know engineering fundamentals. The same arguments hold true across many disciplines -- it can be argued that what the pharmacology industry really needs is biologists who know how to compute, or computational scientists who understand biology.
What is really needed are sophisticated computational science curricula that integrate and use concepts that have been developed in multiple science and engineering domains. For example, simulation and modeling will be applied across science and engineering domains, coupled to visualization, data-mining, and statistical analysis. HPC should be an integral part of upcoming computational science programs and/or integrated in existing computing curricula at the undergraduate levels.
In addition, most engineering and computer science college programs cater to what industry needs now, not what industry might need in the future. Engineering and computer science curricula in most U.S. colleges focus on mainstream industry requirements, and doctoral students in engineering and science are channeled into very specialized domains of discourse -- they are not encouraged to seek the breadth that is needed for computational scientists to be effective.
- It can be argued that many typical programming jobs are now routinely outsourced overseas. Without adding to the controversy surrounding this recent phenomenon, it can be argued that something other than jobs may need to drive the need for more industry-relevant computer science graduates. As Wired reporter Chris Anderson states, computers have always produced creative jobs in the workforce. Even though there is some evidence that some IT work will be brought back to the U.S., an AMR Research Inc. study of 125 large and mid-size companies found that 41% use a mix of offshore and onshore outsourcing, while 17% send all of their IT work overseas (See IT Labor Boomerangs Back Home). So instead of needing workers trained to do relatively straightforward computer tasks, industry might now require a workforce that is trained to take advantage of HPC-enabled innovation.
- Another barrier to the extensive industrial use of HPC is pricing and support for industry computational systems and software. Third-party vendors and their costs for licenses differ from those available for industry users. Academic software licenses are not transferable to commercial use -- even for testing. This situation is a barrier for mid-level companies that wish to engage academia in research that uses HPC for innovative industrial research.
- Intellectual property and the security needed for handling industrial data is yet another barrier. There are documented cases in which legal confidentiality issues have extended the process of providing HPC services to industrial partners by 18 months or more. Thus, industry HPC users have tremendous confidentiality issues to surmount before they can outsource or share the best-equipped HPC centers. Simply stated, most companies are not ready to address the legal and security issues that accrue when they consider moving their most challenging problems to HPC centers.
- Return on investment (ROI) is calculated differently in educational and governmental HPC research labs and industry. Industry's timing is driven by their need for immediate returns and deliverables. Given that most CEOs must answer to shareholders who expect a yearly, if not quarterly, ROI, there is reduced flexibility in embracing long-term solutions to design and production issues. In addition, some companies cannot work within an academic time-share approach for HPC resources.
- Non-Traditional barriers involve culture clashes that occur between industrial and HPC communities. The reason why industry has not tapped the expertise of national or regional HPC centers can be boiled down to a simple issue of tech-transfer. The industrial community is accustomed to solving problems on the desktop, so many industrial leaders -- including those in management, design, production, and testing -- have simply not been exposed to HPC. Little HPC/industry collaboration currently takes place, which is problematic considering how industry might tap HPC experts to help with some of their most challenging problems.
- Another stumbling block may be a lack of imagination. Given the current reliance on desktop computing as the foundation of most industrial communication and computing, it is not surprising that most engineers and designers don't consider what problems could be solved more efficiently, more economically, or more comprehensively by using far more potent computing than that which is found on their desktops.
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