An Adaptive Finite Element Methodology for the High-Performance Computer Simulation of Multiphase Flow Processes.

A methodology has been developed for the computer simulation of multiphase flow processes in porous media. The solutions to the nonlinear equations describing these processes are approximated by Galerkin\u27s method on the spatial dimensions and the finite difference method on the temporal dimension. Due to the transient nature of discontinuities in the spatial domain, dynamic mesh refinement (and unrefinement) techniques, based on the maintenance of a 1-irregular mesh, are employed on a two dimensional mesh to produce fine resolution in regions of activity and coarse resolution elsewhere. Our unique approach is tested by comparing computed results with data from laboratory experiments. The groundwork for extending this approach to three dimensional problems is laid in the development of a new finite element for use in 1-irregular adaptive schemes. We describe the development of this element, prove its correctness, and demonstrate its utility in a test problem. Finally, a three dimensional static-mesh version of the approach is distributed over a cluster of workstations, utilizing PVM for message passing. The repeated solution of large systems of equations dominates the computations, and is the focus of the effort in parallelization. Substructuring techniques are employed, allowing for efficient coarse-grained computations due to the distribution of expensive matrix operations over multiprocessors. An analysis of the performance characteristics of this approach is given, followed by a description of tests on a real-world problem

Biological Networks

Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales—from ecosystems to individual cells and from years to milliseconds. For these networks, the concept “the whole is greater than the sum of its parts” applies as a norm rather than an exception. Meanwhile, continued advances in molecular biology and high-throughput technology have enabled a broad and systematic interrogation of whole-cell networks, allowing the investigation of biological processes and functions at unprecedented breadth and resolution—even down to the single-cell level. The explosion of biological data, especially molecular-level intracellular data, necessitates new paradigms for unraveling the complexity of biological networks and for understanding how biological functions emerge from such networks. These paradigms introduce new challenges related to the analysis of networks in which quantitative approaches such as machine learning and mathematical modeling play an indispensable role. The Special Issue on “Biological Networks” showcases advances in the development and application of in silico network modeling and analysis of biological systems

Optimizing Computations and Allocations on High Performance and Cloud Computing Systems

Over the last decade, many research and development projects have focused on Cloud Computing systems. After forming around the early research papers and the first commercial cloud offerings in 2006-2008, the field has seen a tremendous progress and has provided the primary infrastructure and technology for applications at small, medium, and large scales. Cloud Computing systems have provided diverse on-demand resources to individual researchers and developers, groups and entire institutions, as well as commercial companies and government organizations. Clouds have also found their niche in scientific computing applications, offering attractive alternatives to High Performance Computing models and systems. While cloud economics and technologies have significantly matured recently, there is much active research revolving around topics such as optimality, usability, manageability, and reproducibility in the latest studies. This dissertation presents our findings and relevant developments at the intersection of Cloud Computing and such “flavors” of computing as High Performance Computing and High Throughput Computing. We primarily focus on optimality issues in this area and propose solutions that address the needs of individual researchers and research groups with limited computational and financial resources

Optimizing Computations and Allocations on High Performance and Cloud Computing Systems

Over the last decade, many research and development projects have focused on Cloud Computing systems. After forming around the early research papers and the first commercial cloud offerings in 2006-2008, the field has seen a tremendous progress and has provided the primary infrastructure and technology for applications at small, medium, and large scales. Cloud Computing systems have provided diverse on-demand resources to individual researchers and developers, groups and entire institutions, as well as commercial companies and government organizations. Clouds have also found their niche in scientific computing applications, offering attractive alternatives to High Performance Computing models and systems. While cloud economics and technologies have significantly matured recently, there is much active research revolving around topics such as optimality, usability, manageability, and reproducibility in the latest studies. This dissertation presents our findings and relevant developments at the intersection of Cloud Computing and such “flavors” of computing as High Performance Computing and High Throughput Computing. We primarily focus on optimality issues in this area and propose solutions that address the needs of individual researchers and research groups with limited computational and financial resources