Introduction to Case Studies 



This section presents examples of on-going 

 Grand Challenge applications research and 

 emerging HPCC hardware and software tech- 

 nologies being developed in support of real- 

 world problems and the HPCC Program. 



Only within the past several decades have 

 advances in high performance computing and 

 networking technologies merged with long 

 standing theoretical and experimental methods to 

 yield a powerful new computational approach to 

 scientific inquiry. This approach has enabled 

 scientists and engineers to transcend many of the 

 limitations inherent in the more traditional prac- 

 tices. The demonstrated success of computation- 

 al science in a wide variety of problem areas has 

 led to an infusion of high performance comput- 

 ing technologies and techniques into mainstream 

 scientific practice. An increasing number of 

 researchers, representing almost every disci- 

 pline, continues to probe new and complex prob- 

 lem areas - many of pressing societal concern. 



The computational approach adds another 

 dimension to research methods by allowing the 

 researcher to create a mathematical model of 

 some aspect of reality. Solving the model entails 

 translating its equations into a form capable of 

 being programmed and executed on a high per- 

 formance computing system. Algorithms that 

 structure input data and specify the manner in 

 which the calculations are to be performed are 

 the primary building blocks of the computational 

 model. By exercising the model over broad 

 data ranges and parameter spaces, a picture- 

 albeit a simulated one - of the real phenomena 

 emerges. To the extent that it is complete and 

 accurate, this picture is useful in describing real- 

 ity as it exists and perhaps more importantly, 

 predicting change. In many cases such as those 

 described in this section, scientists are able to 

 reach and extend the understanding of phenome- 

 na far beyond what is possible through pure rea- 

 soning and observation. 



The success of this approach directly depends 

 upon computing and data management capabili- 

 ty. Input data ranges and parameter spaces may 

 be mathematical continua - infinite in dimen- 

 sion. It may require billions of calculations to 

 produce a single solution point, and some phe- 

 nomena require billions of solution points to 

 approach a useful level of completeness and 

 accuracy. Thus the computational requirements 

 of many applications are potentially boundless. 

 Similarly, the "answers" associated with a simu- 

 lation may comprise terabytes of data. Scientific 

 visualization, with its ability to store and inter- 

 pret data, has come to play an indispensable role 

 in the overall success of computational research. 



The strengths and limitations of the computa- 

 tional approach are readily evident: a simulation 

 can represent reality only to the degree that it 

 both holds to the physical laws of nature and 

 captures the inherent complexity and detail of 

 that which it attempts to represent. However, for 

 many problems this is the only approach avail- 

 able. Scientific instruments for observation and 

 measurement confront limits - either those 

 imposed by the nature or location of the object 

 of interest, or by the economics of producing the 

 instrument or conducting the experiment. There 

 are few alternatives to the computational 

 approach for studying aspects of nature lying at 

 the extremes of measurability - those that are 

 very small, very large, very fast, very slow, very 

 close, very far, and so on. Yet the universe is 

 filled with such phenomena that affect our daily 

 lives. 



The examples presented here are only a small 

 subset of the early achievements to come out of 

 the High Performance Computing and 

 Communications Program - a hint of the 

 promise of HPCC methods and technologies 

 applied to long standing problems of science and 

 engineering - and humanity. 



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