Communication Performance Models in Prism: A Spectral Element-Fourier Parallel Navier-Stokes Solver

In this paper we analyze communication patterns in the parallel three-dimensional NavierStokes solver Prism , and present performance results on the IBM SP2, the Cray T3D and the SGI Power Challenge XL. Prism is used for direct numerical simulation of turbulence in non-separable and multiply-connected domains. The numerical method used in the solver is based on mixed spectral element-Fourier expansions in (x \Gamma y) planes and z \Gammadirection, respectively. Each (or a group) of Fourier modes is computed on a separate processor as the linear contributions (Helmholtz solves) are completely uncoupled in the incompressible NavierStokes equations; coupling is obtained via the nonlinear contributions (convective terms). The transfer of data between physical and Fourier space requires a series of complete exchange operations, which dominate the communication cost for small number of processors. As the number of processors increases, global reduction and gather operations become important ..

Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean

Uncertainty prediction for ocean and climate predictions is essential for multiple applications today. Many-Task Computing can play a significant role in making such predictions feasible. In this manuscript, we focus on ocean uncertainty prediction using the Error Subspace Statistical Estimation (ESSE) approach. In ESSE, uncertainties are represented by an error subspace of variable size. To predict these uncertainties, we perturb an initial state based on the initial error subspace and integrate the corresponding ensemble of initial conditions forward in time, including stochastic forcing during each simulation. The dominant error covariance (generated via SVD of the ensemble) is used for data assimilation. The resulting ocean fields are used as inputs for predictions of underwater sound propagation. ESSE is a classic case of Many Task Computing: It uses dynamic heterogeneous workflows and ESSE ensembles are data intensive applications. We first study the execution characteristics of a distributed ESSE workflow on a medium size dedicated cluster, examine in more detail the I/O patterns exhibited and throughputs achieved by its components as well as the overall ensemble performance seen in practice. We then study the performance/usability challenges of employing Amazon EC2 and the Teragrid to augment our ESSE ensembles and provide better solutions faster. Keywords: MTC; assimilation; data-intensive; ensembleUnited States. Office of Naval Research (Grant N00014-08-1-1097)United States. Office of Naval Research (Grant N00014-07-1-0501)United States. Office of Naval Research (Grant N00014-08-1-0586

N.: Rapid real-time interdisciplinary ocean forecasting using adaptive sampling and adaptive modeling and legacy codes: Component encapsulation using XML

Abstract. We present the high level architecture of a real-time interdisciplinary ocean forecasting system that employs adaptive elements in both modeling and sampling. We also discuss an important issue that arises in creating an integrated, web-accessible framework for such a system out of existing stand-alone components: transparent support for handling legacy binaries. Such binaries, that are most common in scientific applications, expect a standard input stream, maybe some command line options, a set of input files and generate a set of output files as well as standard output and error streams. Legacy applications of this form are encapsulated using XML. We present a method that uses XML documents to describe the parameters for executing a binary.

Path planning of autonomous underwater vehicles (AUVs) for adaptive sampling

Abstract—The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constraints of the available observing network. Examples of objectives are better oceanic understanding, to improve forecast quality, or to sample regions of high interest. This work provides a new path-planning scheme for the adaptive sampling problem. We define the path-planning problem in terms of an optimization framework and propose a method based on mixed integer linear programming (MILP). The mathematical goal is to find the vehicle path that maximizes the line integral of the uncertainty of field estimates along this path. Sampling this path can improve the accuracy of the field estimates the most. While achieving this objective, several constraints must be satisfied and are implemented. They relate to vehicle motion, intervehicle coordination, communication, collision avoidance, etc. The MILP formulation is quite powerful to handle different problem constraints and flexible enough to allow easy extensions of the problem. The formulation covers single- and multiple-vehicle cases as well as singleand multiple-day formulations. The need for a multiple-day formulation arises when the ocean sampling mission is optimized for several days ahead. We first introduce the details of the formulation, then elaborate on the objective function and constraints, and finally, present a varied set of examples to illustrate the applicability of the proposed method. Index Terms—Adaptive sampling, Autonomous Ocean Sampling Network (AOSN), autonomous underwater vehicle (AUV), dat

MITgcm User Manual

User manual for the MIT General Circulation Model (MITgcm)

A Computational Model for Overcoming Drug Resistance Using Selective Dual-Inhibitors for Aurora Kinase A and Its T217D Variant

The human Aurora kinase-A (AK-A) is an essential mitotic regulator that is frequently overexpressed in several cancers. The recent development of several novel AK-A inhibitors has been driven by the well-established association of this target with cancer development and progression. However, resistance and cross-reactivity with similar kinases demands an improvement in our understanding of key molecular interactions between the Aurora kinase-A substrate binding pocket and potential inhibitors. Here, we describe the implementation of state-of-the-art virtual screening techniques to discover a novel set of Aurora kinase-A ligands that are predicted to strongly bind not only to the wild type protein, but also to the T217D mutation that exhibits resistance to existing inhibitors. Furthermore, a subset of these computationally screened ligands was shown to be more selective toward the mutant variant over the wild type protein. The description of these selective subsets of ligands provides a unique pharmacological tool for the design of new drug regimens aimed at overcoming both kinase cross-reactivity and drug resistance associated with the Aurora kinase-A T217D mutation