Closing Remarks
What, besides those developments mentioned by other presenters, has or has not changed since 1983? In light of the discussion at this conference about risk taking, I think it is important to remember that there has been significant risk taking involved in supercomputing the last several years. The vendors have taken considerable risks in bringing massively parallel and related products to market. From the perspective of someone who buys 1000-processor systems before one is even built by the vendor, customers have also taken significant risks. Those who funded massively parallel acquisitions in the 1980s have taken risks.
I've seen a great improvement in terms of vendor interest in user input, including input into the design of future systems. This doesn't mean that vendor-user interaction is ideal, but both sides realize that interaction is essential to the viability of the supercomputing industry.
A more recent, encouraging development is the emerging commercial activity in portability. Commercial products like STRAND88 (from Strand Software Technologies Inc.) and Express (from Parasoft Corporation) have appeared. These provide a starting point for code portability, at least between different distributed-memory MIMD machines and perhaps also between distributed-memory and shared-memory machines.
We are much further from achieving portability of Fortran between MIMD and SIMD systems, in part due to the unavailability of Fortran 90 on the former.
Another philosophical point concerns which current systems are supercomputers and which are not. We believe that the era of a single, dominant supercomputer has ended, at least for the 1990s if not permanently. Without naming vendors, I believe that at least four of them have products that qualify as supercomputers in my book—that is, their current system provides the fastest available performance on some portion of the spectrum of computational science and engineering applications. Even given the inevitable industry shakeouts, that is likely to be the situation for the near future.
What hasn't happened in supercomputing since 1983? First, language standards are lagging. Fortran 8X has now become Fortran 90. There are no parallel constructs in it, although we at least get array syntax, which may make for better nonstandard parallel extensions. To some extent, the lack of a parallel standard is bad because it certainly hinders portability. In another sense the lack of a parallel standard is not bad because it's not clear that the Fortran community knows what all the parallel extensions should be and, therefore, what all the new standards should be. I would hate to see a new standard emerge that was focused primarily on SIMD, distributed-memory MIMD, or shared-memory MIMD computing, etc., to the detriment of the other programming models.
A major concern is that massive parallelism does not have buy-in from most computational scientists and engineers. There are at least three good reasons for this. First, recall the concern expressed by many presenters at this conference for education to get more people involved in supercomputing. This is especially true of parallel computing. A second issue is opportunity , i.e., having systems, documentation, experienced users, etc., available to newcomers to smooth their transition into supercomputing and parallel computing. The role of the NSF centers in making vector supercomputers accessible is noteworthy.
The third, and perhaps most critical, issue is interest . We are at a crossroads where a few significant applications on the Connection Machine, nCUBE 2, and Intel iPSC/860 achieve a factor of 10 or more better run-time performance than on vector supercomputers. On the other hand, there is a large body of applications, such as the typical mix of finite-element- and finite-difference-based applications, that typically achieve performance on a current massively parallel system that is comparable to that of a vector supercomputer, or at most three to five times the vector supercomputer. This level of performance is sufficient to
demonstrate a price/performance advantage for the massively parallel system but not a clear raw-performance advantage. In some cases, end users are willing to buy into the newer technology on the basis of the price/performance advantage. More often, there is a great deal of reluctance on the part of potential users.
User buy-in for massive parallelism is not a vector supercomputer versus massively parallel supercomputer issue. In 1983 we faced a similar situation in vector supercomputing: many users did not want to be concerned with vector processors and how one gets optimum performance out of them. In recent years the situation has gradually improved. The bottom line is that eventually most people who are computational scientists at heart come around, and a few get left behind. In summary, I hope that someday all computational scientists in the computational science and engineering community will consider advanced computing to be part of their career and part of their job.