Advanced Software Technology and 

 Algorithms (ASTA) 



The purpose of the ASTA component is to develop the scalable parallel algorithms and software need- 

 ed to realize the potential of the new high performance computing systems in solving Grand 

 Challenge problems in science and engineering. The early experimental use of this software on the 

 new systems accelerates their maturation and identifies and resolves scaling issues on the most chal- 

 lenging problems. 



The principal objectives of the ASTA component are to: 



ci Demonstrate large-scale, multidisciplinary computational results on heterogeneous, distributed 

 systems, using the Internet to access distributed file systems and national software libraries. 



Q Establish portable, scalable libraries that enable transition to the new scalable computing base 

 across different systems and their continued advance through successive generations. 



Q Develop a suite of software tools that enhance productivity (e.g., debuggers, monitoring and par- 

 allelization tools, run-time optimizers, load balancing tools, and data management and visualiza- 

 tion tools). 



'^ Promote broad industry involvement. 



The ASTA component is composed of four elements: 



I. Support for Grand Challenges 



Prototype applications software will be developed to address computationally intensive problems such 

 as the Grand Challenges. Solution of these problems is not only critical to the missions of agencies in 

 the HPCC Program, but has broad applicability to the national technology base. Continuing increases 

 in computational power enable researchers in government, industry, and academia to address prob- 

 lems of greater magnitude and complexity. Increased computational power enables: 



QMore realistic models as a result of higher resolution computational models. An example is 

 weather models that show features on a local or regional scale, not just on a continental or global 

 scale. 



3 Reduced execution times. Models that took days of execution time now take hours, enabling the 

 user to modify the input more frequently, perhaps interactively and graphically, thus gaining 

 insight faster. Reduced execution times also enable modeling over longer time scales (for exam- 

 ple, 100 year climate models can now be executed in the same time it used to take for 10 year 

 models). 



Q More sophisticated models, including models formerly too time consuming. The radiative prop- 

 erties of clouds can be included in climate models, for example. 



Q Lower cost solutions to specific problems, resulting in availability to a larger user community. 



41 



