No abstract available.

The ACTS Collection brings together a number of general-purpose computational tools that were developed by independent research projects mostly funded and supported by the U.S. Department of Energy. These tools tackle a number of common computational issues found in many applications, mainly implementation of numerical algorithms, and support for code development, execution, and optimization. In this article, we introduce the numerical tools in the collection and their functionalities, present a model for developing more complex computational applications on top of ACTS tools, and summarize applications that use these tools. Last, we present a vision of the ACTS project for deployment of the ACTS Collection by the computational sciences community.

General Circulation Models (GCMs) are at the top of the hierarchy of numerical models that are used to study the Earth's climate. To increase the significance of predictions using GCMs requires ensembles of integrations that in turn demand large amounts of computing resources. GCMs codes are particularly difficult to optimize in view of their heterogeneity. In this paper we focus on code optimization for GCMs of the atmosphere (AGCMs), one of the major components of the climate system. In this paper, we present our efforts in optimizing the parallel UCLA AGCM code. The UCLA AGCM is a state-of-the-art finite-difference model of the global atmosphere. Our optimization efforts include the implementation of load balancing schemes, new physical parameterizations of atmospheric processes, code restructuring and use of special mathematical functions. At the beginning of this work, the overall execution time of the code was 459 seconds per simulated day in 256 nodes of a CRAY T3D. At present, the same model configuration requires 51 seconds per simulated day in 256 nodes of a CRAY T3E-900, which is approximately 9 times faster. The peak model performance is about 40 GFLOPs on 512 T3E-900 nodes. We present results in support of our conclusion that major advances in our ability to carry out longer and more detailed climate simulations depend primarily upon development of more powerful supercomputers and that code optimization, for a particular computer architecture, and development of more efficient algorithms can be nearly as important.