Advantages of using adaptive remeshing and parallel processing for modelling biodegradation in groundwater

Recent progress in the algorithm as well as the processor power have made the dynamics simulation of complex kinematic chains more realistic in various fields such as human motion simulation and molecular dynamics. The computation can be further accelerated by employing parallel processing on multiple processors. In fact, parallel processing environment is becoming more affordable thanks to recent release of multiplecore processors. Although several parallel algorithms for the forward dynamics computation have been proposed in literature, there still remains the problem of automatic scheduling, or load distribution, for handling arbitrary kinematic chains on a given parallel processing environment. In this paper, we propose a method for finding the schedule that minimizes the computation time. We test the method using three human character models with different complexities and show that parallel processing on two processors reduces the computation time by 35–36%.

Desktop computers are idle much of the time. Ongoing trends make aggregate LAN waste-idle compute cycles-an increasingly attractive target for recycling. Piranha, a software implementation of adaptive parallelism, allows these waste cycles to be recaptured by putting them to work running parallel applications. Most parallel processing is static: programs execute on a fixed set of processors throughout a computation. Adaptive parallelism allows for dynamic processor sets which means that the number of processors working on a computation may vary, depending on availability. With adaptive parallelism, instead of parceling out jobs to idle workstations, a single job is distributed over many workstations. Adaptive parallelism is potentially valuable on dedicated multiprocessors as well, particularly on massively parallel processors. One key Piranha advantage is that task descriptors, not processes, are the basic movable, remappable computation unit. The task descriptor approach supports strong heterogeneity. A process image representing a task in mid computation can't be moved to a machine of a different type, but a task descriptor can be. Thus, a task begun on a Sun computer can be completed by an IBM machine. The authors show that adaptive parallelism has the potential to integrate heterogeneous platforms seamlessly into a unified computing resource and to permit more efficient sharing of traditional parallel processors than is possible with current systems. >

A programming system has been implemented in which annotated Petri nets are used as machine-processable high-evel design representations. The nets can be used to express the parallelism and the dynamic sequential dependencies found in complex software. They can then be interactively fired to facilitate debugging of the design. The nets are processed into a procedure language, called XL/1, to which a variety of transformations are applied in order to produce more efficient programs. These programs are generated for either a serial or a parallel processing environment. Finally, the XL/1 programs may be translated into PL/I or PL/S. The serial processing versions have been compiled and run successfully, but the parallel processing versions have not yet been run in a parallel processing environment.

This paper proposes an architecture for accomplishing transport protocol processing at Gbps rates. The limitations of currently used transport protocols have been analyzed extensively in recent literature. Several benchmark studies have established the achievable throughput of ISO TP4 and TCP to be in the low Mbps range; several new protocols and implementation techniques have been proposed to achieve 100 Mbps and higher throughput rates. We briefly review some of these protocols and establish the need for a radically different approach to meet our objective. An estimate of the aggregate processing power required for Gbps throughput is developed. It is proposed that a cost effective and practical solution to the processing requirements could be based on a multi-processor system. The opportunities for parallel processing in a typical transport protocol are examined. Several alternate parallel processing approaches are examined and arguments are advanced for selecting a favored approach. A corresponding parallel processing architecture is described. Data structures used to preserve packet ordering and techniques for reducing contention in a multi-processing environment are discussed. An implementation methodology for conventional transport protocols (e.g. TP4) is outlined. Some suggestions are made for improving efficiency by making modifications to the protocol that do not compromise functionality. The performance achievable with this modified architecture is analyzed and some suggestions for further work are presented.

Current and future parallel computing model has been suggested for running and solving large-scale application problems such as climate, bio, cryptology, and astronomy, etc. Parallel computing is a form of computation in which many calculations are carried out simultaneously. And we are able to shorten the execution time of the program, as well as can extend the scale of the problem that can be solved. In this paper, we perform the actual cryptographic algorithms through parallel processing and evaluate its efficiency. Length of the key, which is stable criterion of cryptographic algorithm, judged according to the amount of complete enumeration computation. So we present a detailed procedure of DES key search cryptographic algorithms for executing of enumeration computation in parallel processing environment. And then, we did the simulation through applying to clustering system. As a result, we can measure the safety and solidity of cryptographic algorithm.

SummaryPower system monitoring relies on the reliability of state estimation (SE) results. SE plays a dominant role in data debugging if sufficient data is available. Criticality analysis (CA) integrates SE as a module in which measurements—taken one‐by‐one or in groups (tuples) of minimal cardinality—are designated crucial. The combinatorial nature of extensive CA (not restricted to identifying low‐cardinality critical tuples) characterizes its computational complexity and imposes challenging limits to go beyond. In simple terms, these limits are established by the number of measurements to be combined, the cardinality of tuples, and the computing time required to check the criticality condition. This paper proposes an innovative computational solution to expand CA limits found to date in the literature. A framework with multi‐threads designed cleverly on a graphics processing unit (GPU) parallel processing environment is built. The conceived architecture favors evaluating massive measurement combinations of diverse cardinality in extensive CA. Numerical results reveal significant speed‐ups with the proposed approach, contrasting with those reported in research efforts published so far.

Manipulation of terrain data for a real-time display application

Acceleration of a Navier–Stokes equation solver for unstructured grids using agglomeration multigrid and parallel processing

An analysis of parallelism for modular process flowsheet calculations is given. The central idea is to examine various algorithms that allow one to decouple and decentralize flowsheet calculations for computation in a multiprocessor environment. Specific algorithms are evaluated which make use of quasi‐Newton updates to converge the flowsheet equations. From the analysis presented and example problems solved, it is clear that parallel processing can be used to enhance the computational speed of modular flowsheet calculations. In particular, various formulations of the simultaneous modular approach are analyzed to show how parallelism may be taken advantage of in flowsheet solutions.

The efficient symmetric Gauss-Seidel (SGS) algorithm for solving the Euler equations of inviscid, compressible flow on structured grids, developed in collaboration with Jameson of Stanford University, is extended to unstructured grids. The algorithm uses a nonlinear formulation of an SGS solver, implemented within the framework of multigrid. The earlier form of the algorithm used the natural (lexicographic) ordering of the mesh cells available on structured grids for the SGS sweeps, but a number of features of the method that are believed to contribute to its success can also be implemented for computations on unstructured grids. The present paper reviews, the features of the SGS multigrid solver for structured gr0ids, including its nonlinear implementation, its use of “absolute” Jacobian matrix preconditioning, and its incorporation of multigrid, and then describes the incorporation of these features into an algorithm suitable for computations on unstructured grids. The implementation on unstructured grids is based on the agglomerated multigrid method developed by Sørensen, which uses an explicit Runge-Kutta smoothing algorithm. Results of computations for steady, transonic flows past two-dimensional airfoils are presented, and the efficiency of the method is evaluated for computations on both structured and unstructured meshes.

Hybrid memory systems composed of DRAM and Non-Volatile Memory (NVM) can offer very large memory capacity for data-intensive applications such as graph processing. The performance of parallel graph processing is often affected by straggler tasks due to asymmetrical graph partitioning and sub-graph processing. In hybrid memory systems, the significant difference of performance between DRAM and NVM may exacerbate the load imbalance of parallel graph processing. Traditional load balancing schemes that only aim to balance computing loads among multiple processors are no longer effective in hybrid memory systems.In this paper, we first explore how hybrid memory systems affect the efficiency of existing graph processing systems. We make two observations: 1) Traditional work-stealing schemes may be not efficient enough in hybrid memory systems. Data migration combining with work-stealing can further improve the efficiency of parallel graph processing; 2) Interleaving accesses to different graph partitions due to data dependency have a significant impact on the effectiveness of data migration. To address these problems, we propose NVMGraph, a parallel graph processing scheme to mitigate memory access imbalance among partitions in hybrid memory systems. We first recognize data dependency among all partitions according to graph algorithms and then coalesce the randomly-accessed data required by a working thread in a single partition to mitigate data dependency. To reduce migration cost while still speeding up the straggler task, we only migrate randomly-accessed data blocks in the most frequently-accessed partition from NVM to DRAM. We implement NVMGraph based on Ligra and evaluate it in a hybrid memory system using real NVM devices. Experimental results demonstrate that NVMGraph can improve the performance of graph processing by up to 40.3% compared with the state-of-the-art Ligra and Polymer.

The specter of contaminated groundwater looms over industrialized, suburban, and rural areas. The sources of groundwater contamination are many and the contaminants numerous. Historical waste disposal practices pose the greatest threat to groundwater in the United States. Common solvents such as trichloroethylene, tetrachloroethane, benzene, and carbon tetrachloride have been found in widespread areas. For this study, a risk assessment is performed using a multimedia environmental transport model to estimate public risk from a contaminated groundwater plume. From 1955 to 1973, a crib above the groundwater plume was used for the disposal of plutonium processing waste. The plume contains chemical and radioactive wastes that could pose a health threat to people living in the vicinity of the site. Remedial designs are selected for remediating the different contaminants in the groundwater plume through a multiple phase treatment process. This study evaluates: baseline health risks to the public, health risk reduction to the public as a result of the remedial activities, health risk to the workers directly involved in cleaning up the site, and costs associated with each remedial activity.

An Environment for Knowledge Parallel Processing Used in Process Control

Laplacian matrices of graphs arise in large-scale computational applications such as semisupervised machine learning; spectral clustering of images, genetic data, and web pages; transportation network flows; electrical resistor circuits; and elliptic partial differential equations discretized on unstructured grids with finite elements. A lean algebraic multigrid (LAMG) solver of the symmetric linear system $Ax=b$ is presented, where $A$ is a graph Laplacian. LAMG's run time and storage are empirically demonstrated to scale linearly with the number of edges. LAMG consists of a setup phase, during which a sequence of increasingly coarser Laplacian systems is constructed, and an iterative solve phase using multigrid cycles. General graphs pose algorithmic challenges not encountered in traditional multigrid applications. LAMG combines a lean piecewise-constant interpolation, judicious node aggregation based on a new node proximity measure (the affinity), and an energy correction of coarse-level systems. This results in fast convergence and substantial setup and memory savings. A serial LAMG implementation scaled linearly for a diverse set of 3774 real-world graphs with up to 47 million edges, with no parameter tuning. LAMG was more robust than the UMFPACK direct solver and combinatorial multigrid (CMG), although CMG was faster than LAMG on average. Our methodology is extensible to eigenproblems and other graph computations.

A nested multi-grid solution algorithm has been developed for an adaptive Cartesian/Quad grid viscous flow solver. Body-fitted adaptive Quad (quadrilateral) grids are generated around solid bodies through ‘surface extrusion’. The Quad grids are then overlapped with an adaptive Cartesian grid. Quadtree data structures are employed to record both the Quad and Cartesian grids. The Cartesian grid is generated through recursive sub-division of a single root, whereas the Quad grids start from multiple roots—a forest of Quadtrees, representing the coarsest possible Quad grids. Cell-cutting is performed at the Cartesian/Quad grid interface to merge the Cartesian and Quad grids into a single unstructured grid with arbitrary cell topologies (i.e., arbitrary polygons). Because of the hierarchical nature of the data structure, many levels of coarse grids have already been built in. The coarsening of the unstructured grid is based on the Quadtree data structure through reverse tree traversal. Issues arising from grid coarsening are discussed and solutions are developed. The flow solver is based on a cell-centered finite volume discretization, Roe's flux splitting, a least-squares linear reconstruction, and a differentiable limiter developed by Venkatakrishnan in a modified form. A local time stepping scheme is used to handle very small cut cells produced in cell-cutting. Several cycling strategies, such as the saw-tooth, W- and V-cycles, have been studies. The V-cycle has been found to be the most efficient. In general, the multi-grid solution algorithm has been shown to greatly speed up convergence to steady state—by one to two orders. Copyright © 2000 John Wiley & Sons, Ltd.