Large-scale simulation codes typically execute for extended periods of time and often on distributed computational resources. Because these simulations can run for hours, or even days, scientists like to get feedback about the state of the computation and the validity of its results as it runs. It is also important that these capabilities be made available with little impact on the performance and stability of the simulation. Visualizing and exploring data in the early stages of the simulation can help scientists identify problems early, potentially avoiding a situation where a simulation runs for several days, only to discover that an error with an input parameter caused both time and resources to be wasted. We describe an application that aids in the monitoring and analysis of a simulation of the human arterial tree. The application provides researchers with high-level feedback about the state of the ongoing simulation and enables them to investigate particular areas of interest in greater detail. The application also offers monitoring information about the amount of data produced and data transfer performance among the various components of the application.

Computational grids and grid middleware offer unprecedented computational power and storage capacity, and thus, have opened the possibility of solving problems that was previously not possible on even the largest single computational resources. These opportunities notwithstanding, the development of grid applications that run efficiently remains a challenge due to the inherent heterogeneity of networks and system architectures inherent in such environments. We present grid solutions to two grand challenge problems in computational mechanics. To study the scalability of our solutions we implemented both as MPI applications and ran them on the TeraGrid using NEKTAR and MPICH-G2. We present the results of our study which demonstrate near linear scalability in both applications when run across multiple TeraGrid sites and at a scale of hundreds or processors

The human arterial tree consists of a complex network of branching blood vessels leading from the heart to arterioles, capillaries, and venules - comprising the microcirculation. The numerical simulation of the blood flow in a single part of the human arterial tree requires hundreds of CPUs; a full human arterial tree will require thousands of CPUs. Nowadays, we can use geographically distributed supercomputers connected by a fast network to perform large-scale simulations.Nektar-G2 is the grid-enabled version of Nektar, software developed at Brown University, that allows to solve problems on geographically distributed supercomputers. The topology-aware feature of MPICH-G2 is utilized to enforce an efficient data distribution strategy. Multi-level message passing algorithms minimizes the inter-site communication. Our ultimate goal is to model blood flow interaction of different regions of the cardiovascular system and to establish a biomechanics gateway on the TeraGrid.During poster presentation we will present results of ongoing project.

A hybrid two-level parallelism using MPI/OpenMP is implemented in the general-purpose spectral/hp element CFD code NekTar to take advantage of the hierarchical structures arising in deterministic and stochastic CFD problems. We take a coarse grain approach to shared-memory parallelism with OpenMP and employ a workload-splitting scheme that can reduce the OpenMP synchronizations to the minimum. The hybrid implementation shows good scalability with respect to both the problem size and the number of processors in case of a fixed problem size. With the same number of processors, the hybrid model with 2 (or 4) OpenMP threads per MPI process is observed to perform better than pure MPI and pure OpenMP on the NCSA SGI Origin 2000, while the pure MPI model performs the best on the IBM SP3 at SDSC and on the Compaq Alpha cluster at PSC. A key new result is that the use of threads facilitates effectively p-refinement, which is crucial to adaptive discretization using high-order methods.