Less Is More

Our fixation on the supercomputer as the dominant form of technical computing is finally giving way to reality. Supers are being supplanted by a host of alternative forms of computing, including the interactive, distributed, and personal approaches that use PCs and workstations. The technical computing industry and the community it serves are poised for an exciting period of growth and change in the 1990s.

Traditional supercomputers are becoming less relevant to scientific computing, and as a result, the growth in the traditional vector supercomputer market, as defined by Cray Research, Inc., is reduced from what it was in the early 1980s. Large-system users and the government, who are concerned about the loss of U.S. technical supremacy in this last niche of computing, are the last to see the shift. The loss of supremacy in supercomputers should be of grave concern to the U.S. government, which relies on supercomputers and, thus, should worry about the loss of one more manufacturing-based technology. In the case of supercomputers, having the second-best semiconductors and high-density packaging means that U.S. supercomputers will be second.

The shift away from expensive, highly centralized, time-shared supercomputers for high-performance computing began in the 1980s. The shift is similar to the shift away from traditional mainframes and minicomputers to workstations and PCs. In response to technological advances, specialized architectures, the dictates of economies, and the growing importance of interactivity and visualization, newly formed companies challenged the conventional high-end machines by introducing a host of supersubstitutes: minisupercomputers, graphics supercomputers, superworkstations, and specialized parallel computers. Cost-effective FLOPS, that is, the floating-point operations per second essential to high-performance technical computing, come in many new forms. The compute power for demanding scientific and engineering challenges could be found across a whole spectrum of machines with a range of price/performance points. Machines as varied as a Sun Microsystems, Inc., workstation, a graphics supercomputer, a minisupercomputer, or a special-purpose computer like a Thinking Machines Corporation Connection Machine or an nCUBE Corporation Hypercube all do the same computation for five to 50 per cent of the cost of doing it on a conventional supercomputer. Evidence of this trend abounds.

An example of cost effectiveness would be the results of the PERFECT (Performance Evaluation for Cost-Effective Transformation) contest. This benchmark suite, developed at the University of Illinois Supercomputing Research and Development Center in conjunction with manufacturers and users, attempts to measure supercomputer performance and cost effectiveness.

In the 1989 contest, an eight-processor CRAY Y-MP/832 took the laurels for peak performance by achieving 22 and 120 MFLOPS (million floating-point operations per second) for the unoptimized baseline and hand-tuned, highly optimized programs, respectively. A uniprocessor Stardent Computer 3000 graphics supercomputer won the

cost/performance award by a factor of 1.8 and performed at 4.2 MFLOPS with no tuning and 4.4 MFLOPS with tuning. The untuned programs on the Stardent 3000 were a factor of 27 times more cost effective than the untuned CRAY Y-MP programs. In comparison, a Sun SPARCstation 1 ran the benchmarks roughly one-third as fast as the Stardent.

The PERFECT results typify "dis-economy" of scale. When it comes to getting high-performance computation for scientific and engineering problems, the biggest machine is rarely the most cost effective. This concept runs counter to the myth created in the 1960s known as Grosch's Law, which stated that the power of a computer increased as its price squared. Many studies have shown that the power of a computer increased at most as the price raised to the 0.8 power—a dis-economy of scale.

Table 1 provides a picture of the various computing power and capacity measures for various types of computers that can substitute for supercomputers. The computer's peak power and LINPACK 1K × 1K estimate the peak power that a computer might deliver on a highly parallel application. LINPACK 100-×-100 shows the power that might be expected for a typical supercomputer application and the average speed at which a supercomputer might operate. The Livermore Fortran Kernels (LFKs) were designed to typify workload, that is, the capacity of a computer operating at Lawrence Livermore National Laboratory.

The researchers who use NSF's five supercomputing centers at no cost are insulated from cost considerations. Users get relatively little processing power per year despite the availability of the equivalent 30 CRAY

X-MP processors, or 240,000 processor hours per year. When that processing power is spread out among 10,000 researchers, it averages out to just 24 hours per year, or about what a high-power PC can deliver in a month. Fortunately, a few dozen projects get 1000 hours per year. Moreover, users have to contend with a total solution time disproportionate to actual computation time, centralized management and allocation of resources, the need to understand vectorization and parallelization to utilize the processors effectively (including memory hierarchies), and other issues.

These large, central facilities are not necessarily flawed as a source of computer power unless they attempt to be a one-stop solution. They may be the best resource for the very largest users with large, highly tuned parallel programs that may require large memories, file capacity of tens or hundreds of gigabytes, the availability of archive files, and the sharing of large databases and large programs. They also suffice for the occasional user who needs only a few hours of computing a year and doesn't want to own or operate a computer.

But they're not particularly well suited to the needs of the majority of users working on a particular engineering or scientific problem that is embodied in a program model. They lack the interactive and visualization capabilities that computer-aided design requires, for example. As a result, even with free computer time, only a small fraction of the research community, between five and 10 per cent, uses the NSF centers. Instead, users are buying smaller computing resources to make more power available than the large, traditional, centralized supercomputer supplies. Ironic though it may seem, less is more .