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Overview of Industrial Supercomputing
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Why Use Supercomputing at All?

Before we can analyze the inhibitors to the use of supercomputing, we must have a common understanding of the need for supercomputing. First, the term supercomputer has become overused to the point of being meaningless, as was indicated in remarks by several at this conference. By a supercomputer we mean the fastest, most capable machine available by the only measure that is meaningful—sustained performance on an industrial application of competitive importance to the industry in question. The issue is not which machine is best, at this point, but that some machines or group of machines are more capable than most others, and this class we shall refer to as "supercomputers." Today this class is viewed as large vector computers with a modest amount of parallelism, but the future promises to be more complicated, since one general type of previous hit architecture next hit probably won't dominate the market.

In the aerospace industry, there are traditional workhorse applications, such as aerodynamics, structural analysis, electromagnetics, circuit design, and a few others. Most of these programs analyze a design. One creates a geometric description of a wing, for example, and then analyzes the flow over the wing. We know that today supercomputers cannot handle this problem in its full complexity of geometry and physics. We use simplifications in the model and solve approximations as best we can. Thus, the traditional drivers for more computational power still exist. Smaller problems can be run on workstations, but "new insights" can only be achieved with increased computing power.

A new generation of computational challenges face us as well (Neves and Kowalik 1989). We need not simply analysis programs but also design programs. Let's consider three examples of challenging computing processes. First, consider a program in which one could input a desired shock wave and an initial geometric configuration of a wing and have the optimal wing geometry calculated to most closely simulate the desired shock (or pressure profile). With this capability we could greatly reduce the wing design cycle time and improve product quality. In fact, we could reduce serious flutter problems early in the design and reduce risk of failure and fatigue in the finished product. This type of computation would have today's supercomputing applications as "inner loops" of a design system requiring much more computing power than available today. A second example comes from manufacturing. It is not unusual for a finalized design to be forwarded to manufacturing just to find out that the design cannot be manufactured "as designed" for some


unanticipated reason. Manufacturability, reliability, and maintainability constraints need to be "designed into" the product, not discovered downstream. This design/build concept opens a whole new aspect of computation that we can't touch with today's computing equipment or approaches. Finally, consider the combination of many disciplines that today are separate elements in design. Aerodynamics, structural analyses, thermal effects, and control systems all could and should be combined in design evaluation and not considered separately. To solve these problems, computing power of greater capability is required; in fact, the more computing power, the "better" the product! It is not a question of being able to use a workstation to solve these problems. The question is, can a corporation afford to allow products to be designed on workstations (with yesterday's techniques) while competitors are solving for optimal designs with supercomputers?

Given the rich demand for computational power to advance science and engineering research, design, and analysis as described above, it would seem that there would be no end to the rate at which supercomputers could be sold. Indeed, technically there is no end to the appetite for more power, but in reality each new quantum jump in computational power at a given location (user community) will satisfy needs for some amount of time before a new machine can be justified. The strength in the supercomputer market in the 1980s came from two sources: existing customers and "new" industries. Petrochemical industries, closely followed by the aerospace industry, were the early recruits. These industries seem to establish a direct connection between profit and/or productivity and computing power. Most companies in these industries not only bought machines but upgraded to next-generation machines within about five years. This alone established an upswing in the supercomputing market when matched by the already strong government laboratory market from whence supercomputers sprang. Industry by industry, market penetration was made by companies like Cray Research, Inc. In 1983 the Japanese entered the market, and several of their companies did well outside the U.S. New market industries worldwide included weather prediction, automobiles, chemicals, pharmaceuticals, academic research institutions (state- and NSF-supported), and biological and environmental sciences. The rapid addition of "new" markets by industries created a phenomenal growth rate.

In 1989 the pace of sales slackened at the high end. The reasons are complex and varied, partly because of the options for users with "less than supercomputer problems" to find cost-effective alternatives; but the biggest impact, in my opinion, is the inability to create new industry


markets. Most of the main technically oriented industries are already involved in supercomputing, and the pace of sales has slowed to that of upgrades to support the traditional analysis computations alluded to above. This is critical to the success of these companies but has definitely slowed the rate of sales enjoyed in the 1980s. This might seem like a bleak picture if it weren't for one thing: as important as these traditional applications are, they are but the tip of the iceberg of scientific computing opportunities in industry . In fact, at Boeing well over a billion dollars are invested in computing hardware. Supercomputers have made a very small "dent" in this computing budget. One might say that even though supercomputers exist at almost 100 per cent penetration by company in aerospace, within companies, this penetration is less than five per cent.

Certainly supercomputers are not fit for all computing applications in large manufacturing companies. However, the acceptance of any computing tool, or research tool such as a wind tunnel, is a function of its contribution to the "bottom line." The bottom line is profit margin and market share. To gain market share you must have the "best product at the least cost." Supercomputing is often associated with design and hence, product quality. The new applications of concurrent engineering (multidisciplinary analysis) and optimal design (described above) will achieve cost reduction by ensuring that manufacturability, reliability, and maintainability are included in the design. This story needs to be technically developed and understood by both scientists and management. The real untapped market, however, lies in bringing high-end computation to bear on manufacturing problems ignored so far by both technologists and management in private industry.

For example, recently at Boeing we established a Computational Modeling Initiative to discover new ways in which the bottom line can be helped by computing technology. In a recent pilot study, we examined the rivet-forming process. Riveting is a critical part of airplane manufacturing. A good rivet is needed if fatigue and corrosion are to be minimized. Little is known about this process other than experimental data. By simulating the riveting process and animating it for slow-motion replay, we have utilized computing to simulate and display what cannot be seen experimentally. Improved rivet design to reduce strain during the riveting has resulted in immediate payoff during manufacturing and greatly reduced maintenance cost over the life of the plane. Note that this contributes very directly to the bottom line and is an easily understood contribution. We feel that these types of applications (which in this case required a supercomputer to handle the complex structural analysis simulation) could fill many supercomputers productively once the


applications are found and implemented. This latent market for computation within the manufacturing sectors of existing supercomputer industries is potentially bigger than supercomputing use today. The list of opportunities is enormous: robotics simulation and design, factory scheduling, statistical tolerance analysis, electronic mockup (of parts, assemblies, products, and tooling), discrete simulation of assembly, spares inventory (just-in-time analysis of large, complex manufacturing systems), and a host of others.

We have identified three critical drivers for a successful supercomputing market that all are critical for U.S. industrial competitiveness: 1) traditional and more refined analysis; 2) design optimization, multidisplinary analysis, and concurrent engineering (design/build); and 3) new applications of computation to manufacturing process productivity.

The opportunities in item 3 above are so varied, even at a large company like Boeing, it is hard to be explicit. In fact, the situation requires those involved in the processes to define such opportunities. In many cases, the use of computation is traditionally foreign to the manufacturing process, which is often a "build and test" methodology, and this makes the discovery of computational opportunities difficult. What is clear, however, is that supercomputing opportunities exist (i.e., a significant contribution can be made to increased profit, market share, or quality of products through supercomputing). It is worthwhile to point out broadly where supercomputing has missed its opportunities in most industries, but certainly in the aerospace sector:

• manufacturing—e.g., rivet-forming simulation, composite material properties;

• CAD/CAM—e.g., electronic mockup, virtual reality, interference modeling, animated inspection of assembled parts;

• common product data storage—e.g., geometric-model to grid-model translation; and

• grand-challenge problems—e.g., concurrent engineering, data transfer: IGES, PDES, CALS.

In each area above, supercomputing has a role. That role is often not central to the area but critical in improving the process. For example, supercomputers today are not very good database machines, yet much of the engineering data stored in, say, the definition of an airplane is required for downstream analysis in which supercomputing can play a role. Because supercomputers are not easily interfaced to corporate data farms, much of that analysis is often done on slower equipment, to the detriment of cost and productivity.


With this as a basis, how can there be any softness in the supercomputer market? Clearly, supercomputers are fundamental to competitiveness, or are they?

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