Supercomputer

Nelson, M., Humphrey, W., Gursoy, A., Dalke, A., Kale, L., Skeel, R.D., Schul-ten, K. NAMD - A parallel, object-oriented molecular dynamics program. Int. J. Supercomputing Applications and High Performance Computing 10 (1996) 251-268. 

This work was supported by a postdoctoral NATO fellowship to VH by the Deutscher Akademischer Austauschdienst, and by grants from NIH, NSF, and the NSF Supercomputer Centers MetaCenter program. VH is also a fellow of the Program in Mathematics and Molecular Biology and of the La Jolla Interfaces in Sciences Program. 

TES gratefully acknowledges the Alfred P. Sloan Foundation for support and the National Science Foundation for support (CHE-9632236) and computational resources at the National Center for Supercomputing Applications (CHE-960010N). 

K. Schulten. NAMD—a parallel, object-oriented molecular dynamics program. Inti. J. Supercomput. Applies. High Performance Computing, 10 251-268, 1996. 

Supported by NSF ASC-9318159, NSF CDA-9422065, NTH Research Resource RR08102, and computer time from the North Carolina Supercomputing Center. An earlier version of this paper was presented at the Eighth SIAM Conference on Parallel Processing for Scientific Computing. 

Lin, M., Hsieh, J., Du, D. H. C., Thomas, J. P., MacDonald, J. A. Distributed network computing over local ATM networks. In Proceedings of Supercomputing 94. IEEE Computer Society Press, Los Alamitos, California, 1994. Greengard, L., Rokhlin, V. A fast algorithm for particle simulation. J. Comp. Phys. 73 (1987) 325-348. 

It is a truism (cliche ) that microcomputers have beeome more powerful on an almost exponential eurve sinee their advent more than 30 years ago. Molecular orbital calculations that I ran on a supercomputer a decade ago now run on a fast desktop mierocomputer available at a modest priee in any popular eleetionies store or by mail order eatalog. With this has eome a eomparable inerease in software sophistication. 

Quantum mechanics gives a mathematical description of the behavior of electrons that has never been found to be wrong. However, the quantum mechanical equations have never been solved exactly for any chemical system other than the hydrogen atom. Thus, the entire held of computational chemistry is built around approximate solutions. Some of these solutions are very crude and others are expected to be more accurate than any experiment that has yet been conducted. There are several implications of this situation. First, computational chemists require a knowledge of each approximation being used and how accurate the results are expected to be. Second, obtaining very accurate results requires extremely powerful computers. Third, if the equations can be solved analytically, much of the work now done on supercomputers could be performed faster and more accurately on a PC. 

Another important consideration is the amount of labor necessary on the part of the user. One major difference between different software packages is the developer s choices between ease of use and efficiency of operation. For example, the Spartan program is extremely easy to use, but the price for this is that the algorithms are not always the most efficient available. Many chemistry users begin with software that is very simple, but when more sophisticated problems need to be solved, it is often easier to learn to use more complicated software than to purchase a supercomputer to solve a problem that could be done by a workstation with different software. 

Mass-produced workstation-class CPUs are much cheaper than traditional supercomputer processors. Thus, a larger amount of computing power for the dollar can be purchased by buying a parallel supercomputer that might have hundreds of workstation CPUs. 

Correlated calculations, such as configuration interaction, DFT, MPn, and coupled cluster calculations, can be used to model small organic molecules with high-end workstations or supercomputers. These are some of the most accurate calculations done routinely. Correlation is not usually required for qualitative or even quantitative results for organic molecules. It is needed to obtain high-accuracy quantitative results. 

This book grew out of a collection of technical-support web pages. Those pages were also posted to the computational chemistry list server maintained by the Ohio Supercomputer Center. Many useful comments came from the subscribers of that list. In addition, thanks go to Dr. James F. Harrison at Michigan State University for providing advice born of experience. 

A parallel but more historically comprehensive discussion of glass stmcture and composition has been given (36). Prediction of stmctural parameters and consequent properties from theoretical principles has increased with the advent of supercomputers. Of particular interest to glass scientists... 

The secondary stmcture elements are then identified, and finally, the three-dimensional protein stmcture is obtained from the measured interproton distances and torsion angle parameters. This procedure requites a minimum of two days of nmr instmment time per sample, because two pulse delays are requited in the 3-D experiment. In addition, approximately 20 hours of computing time, using a supercomputer, is necessary for the calculations. Nevertheless, protein stmcture can be assigned using 3-D nmr and a resolution of 0.2 nanometers is achievable. The largest protein characterized by nmr at this writing contained 43 amino acid units (51). However, attempts ate underway to characterize the stmcture of interleukin 2 [85898-30-2] which has over 150 amino acid units. 

Supercomputers, such as the CRAY X-MP, CRAY Y-MP, and CRAY-2, are partially available and used for flow-sheet and optimization studies (7—10). Optimization modules usiag linear and nonlinear programming (LINPRO and UNLPl, based on a revised simplex, and Davidson-Eletcher-PoweU and Broyden methods, respectively) are available ia MicroMENTOR (11). 

The years since pubHcation of the third edition of the Eniyclopedia (1978—1984) have brought the rise and fall of the minicomputer, the worldwide ascendancy of microprocessor-based personal computers, the emergence of powerhil scientific work stations, the acceptance of scientific visualization, further advances with supercomputers, the rise and fall of the rninisupercomputer, and the realization that the future Hes in parallel computing. 

Supercomputers are found in many government research laboratories, intelligence agencies, universities, and a small number of industrial companies. In the United States, the National Science Foundation (NSF) has provided supercomputers to several prominent universities for both academic and industrial users. These centers provide state-of-the-art, supercomputer-tuned appHcations for a wide variety of disciplines, together with staffs who are very knowledgeable in optimization for supercomputer performance. 

A common acronym is MFLOPS, millions of floating-point operations per second. Because most scientific computations are limited by the speed at which floating point operations can be performed, this is a common measure of peak computing speed. Supercomputers of 1991 offered peak speeds of 1000 MFLOPS (1 GFLOP) and higher. 

Vector Computers. Most computers considered supercomputers are vector-architecture computers. The concept of vector architecture has been a source of much confusion. 

Banked Memory. Another characteristic of many vector supercomputers is banked memory. The main memory is usually divided into a small number of electronically separate banks. A given memory bank can absorb or supply operands at a much slower rate than the rate at which the central processing unit (CPU) can produce or use data. If the data can be spread across multiple memory banks, the effective memory bandwidth, or rate at which memory can absorb or supply data, is increased. For example, if a single memory bank can supply one operand every 16 clock cycles, then 16 memory banks would enable the entire memory subsystem to deflver one operand per clock cycle, assuming that the data come sequentially from different memory banks. 

The memory subsystem on most supercomputers is organized to support maximum performance on loops of stride one, or when the elements of an array are accessed sequentially with no gaps. In general, the stride is defined by... 

Supercomputers from vendors such as Cray, NEC, and Eujitsu typically consist of between one and eight processors in a shared memory architecture. Peak vector speeds of over 1 GELOP (1000 MELOPS) per processor are now available. Main memories of 1 gigabyte (1000 megabytes) and more are also available. If multiple processors can be tied together to simultaneously work on one problem, substantially greater peak speeds are available. This situation will be further examined in the section on parallel computers. 

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