Spatio‐Temporal Contours from Deep Volume Raycasting

We visualize contours for spatio‐temporal processes to indicate where and when non‐continuous changes occur or spatial bounds are encountered. All time steps are comprised densely in one visualization, with contours allowing to efficiently analyze processes in the data even in case of spatial or temporal overlap. Contours are determined on the basis of deep raycasting that collects samples across time and depth along each ray. For each sample along a ray, its closest neighbors from adjacent rays are identified, considering time, depth, and value in the process. Large distances are represented as contours in image space, using color to indicate temporal occurrence. This contour representation can easily be combined with volume rendering‐based techniques, providing both full spatial detail for individual time steps and an outline of the whole time series in one view. Our view‐dependent technique supports efficient progressive computation, and requires no prior assumptions regarding the shape or nature of processes in the data. We discuss and demonstrate the performance and utility of our approach via a variety of data sets, comparison and combination with an alternative technique, and feedback by a domain scientist.     

Real‐time Indirect Illumination of Emissive Inhomogeneous Volumes using Layered Polygonal Area Lights

Indirect illumination involving with visually rich participating media such as turbulent smoke and loud explosions contributes significantly to the appearances of other objects in a rendering scene. However, previous real‐time techniques have focused only on the appearances of the media directly visible from the viewer. Specifically, appearances that can be indirectly seen over reflective surfaces have not attracted much attention. In this paper, we present a real‐time rendering technique for such indirect views that involves the participating media. To achieve real‐time performance for computing indirect views, we leverage layered polygonal area lights (LPALs) that can be obtained by slicing the media into multiple flat layers. Using this representation, radiance entering each surface point from each slice of the volume is analytically evaluated to achieve instant calculation. The analytic solution can be derived for standard bidirectional reflectance distribution functions (BRDFs) based on the microfacet theory. Accordingly, our method is sufficiently robust to work on surfaces with arbitrary shapes and roughness values. In addition, we propose a quadrature method for more accurate rendering of scenes with dense volumes, and a transformation of the domain of volumes to simplify the calculation and implementation of the proposed method. By taking advantage of these computation techniques, the proposed method achieves real‐time rendering of indirect illumination for emissive volumes.     

Balancing conflicting requirements for grid and particle decomposition in continuum-Lagrangian solvers

Load balancing strategies for hybrid solvers that involve grid based partial differential equation solution coupled with particle tracking are presented in this paper. A typical Message Passing Interface (MPI) based parallelization of grid based solves are done using a spatial domain decomposition while particle tracking is primarily done using either of the two techniques. One of the techniques is to distribute the particles to MPI ranks to whose grid they belong to while the other is to share the particles equally among all ranks, irrespective of their spatial location. The former technique provides spatial locality for field interpolation but cannot assure load balance in terms of number of particles, which is achieved by the latter. The two techniques are compared for a case of particle tracking in a homogeneous isotropic turbulence box as well as a turbulent jet case. A strong scaling study is performed to more than 32,000 cores, which results in particle densities representative of anticipated exascale machines. The use of alternative implementations of MPI collectives and efficient load equalization strategies are studied to reduce data communication overheads.     

Exploring parallelization techniques based on OpenMP in H.264/AVC encoder for embedded multi-core processor

Recent advances in semiconductor technologies make it possible to integrate many processor cores in a small device package. The parallel execution capability of such multi-core processors can be exploited to enhance the performance of many traditional sequential applications. There have been numerous research activities to develop parallelization techniques using the OpenMp programming model, in order to speed up sequential applications such as the H.264/AVC codec, but mostly in the PC environment. Therefore, it is difficult to understand which parallelization technique fits well with the H.264/AVC encoder on an embedded multi-core architecture. In this paper, we present parallelization techniques applicable to the H.264/AVC encoder on ARM MPCore using the OpenMP programming model. Further, we propose an analytical model for the performance estimation of the H.264/AVC encoder, and we then verify the model accuracy by performing simulations using hardware/software co-verification tool. Our experimental results show that the parallelization techniques proposed in this paper for the embedded multi-core platform improve the encoder performance by up to 2.36 times, and that the parallelization technique exploiting data-level parallelism outperforms the one using task-level parallelism by 41%. It is also observed that balancing loads among processor cores is a critical parameter in achieving better scalability in the encoder.     

Cuckoo and krill herd‐based k‐means++ hybrid algorithms for clustering

Clustering algorithms can be optimized using nature‐inspired techniques. Many algorithms inspired by nature, namely, firefly algorithm, ant colony optimization algorithm, and so forth, have improved clustering results. k‐means is a popular clustering technique but has limitations of local optima, which have been overcome using its various hybrids. k‐means++ is a hybrid k‐means clustering algorithm that gives the procedure to initialize centre of the clusters. In the proposed work, hybrids of nature‐inspired techniques using cuckoo and krill herd algorithm are implemented on k‐means++ algorithm to enhance cluster quality and generate optimized clusters. The designed algorithms are implemented, and the results are compared with their counterparts. Performance parameters such as accuracy, f‐measure, error rate, standard deviation, CPU time, cluster quality check, and so forth are used to measure the clustering capabilities of these algorithms. The results indicate the high performance of newly designed algorithms.     

Quantitative and Qualitative Analysis of the Perception of Semi‐Transparent Structures in Direct Volume Rendering

Direct Volume Rendering (DVR) provides the possibility to visualize volumetric data sets as they occur in many scientific disciplines. With DVR semi‐transparency is facilitated to convey the complexity of the data. Unfortunately, semi‐transparency introduces challenges in spatial comprehension of the data, as the ambiguities inherent to semi‐transparent representations affect spatial comprehension. Accordingly, many techniques have been introduced to enhance the spatial comprehension of DVR images. In this paper, we present our findings obtained from two evaluations investigating the perception of semi‐transparent structures from volume rendered images. We have conducted a user evaluation in which we have compared standard DVR with five techniques previously proposed to enhance the spatial comprehension of DVR images. In this study, we investigated the perceptual performance of these techniques and have compared them against each other in a large‐scale quantitative user study with 300 participants. Each participant completed micro‐tasks designed such that the aggregated feedback gives insight on how well these techniques aid the user to perceive depth and shape of objects. To further clarify the findings, we conducted a qualitative evaluation in which we interviewed three experienced visualization researchers, in order to find out if we can identify the benefits and shortcomings of the individual techniques.     

Parallel-multigrid computation of unsteady incompressible viscous flows using a matrix-free implicit method and high-resolution characteristics-based scheme

A three-dimensional parallel unstructured non-nested multigrid solver for solutions of unsteady incompressible viscous flow is developed and validated. The finite-volume Navier–Stokes solver is based on the artificial compressibility approach with a high-resolution method of characteristics-based scheme for handling convection terms. The unsteady flow is calculated with a matrix-free implicit dual time stepping scheme. The parallelization of the multigrid solver is achieved by multigrid domain decomposition approach (MG-DD), using single program multiple data (SPMD) and multiple instruction multiple data (MIMD) programming paradigm. There are two parallelization strategies proposed in this work, first strategy is a one-level parallelization strategy using geometric domain decomposition technique alone, second strategy is a two-level parallelization strategy that consists of a hybrid of both geometric domain decomposition and data decomposition techniques. Message-passing interface (MPI) and OpenMP standard are used to communicate data between processors and decompose loop iterations arrays, respectively. The parallel-multigrid code is used to simulate both steady and unsteady incompressible viscous flows over a circular cylinder and a lid-driven cavity flow. A maximum speedup of 22.5 could be achieved on 32 processors, for instance, the lid-driven cavity flow of Re = 1000. The results obtained agree well with numerical solutions obtained by other researchers as well as experimental measurements. A detailed study of the time step size and number of pseudo-sub-iterations per time step required for simulating unsteady flow are presented in this paper.     

A General Illumination Model for Molecular Visualization

Several visual representations have been developed over the years to visualize molecular structures, and to enable a better understanding of their underlying chemical processes. Today, the most frequently used atom‐based representations are the Space‐filling, the Solvent Excluded Surface, the Balls‐and‐Sticks, and the Licorice models. While each of these representations has its individual benefits, when applied to large‐scale models spatial arrangements can be difficult to interpret when employing current visualization techniques. In the past it has been shown that global illumination techniques improve the perception of molecular visualizations; unfortunately existing approaches are tailored towards a single visual representation. We propose a general illumination model for molecular visualization that is valid for different representations. With our illumination model, it becomes possible, for the first time, to achieve consistent illumination among all atom‐based molecular representations. The proposed model can be further evaluated in real‐time, as it employs an analytical solution to simulate diffuse light interactions between objects. To be able to derive such a solution for the rather complicated and diverse visual representations, we propose the use of regression analysis together with adapted parameter sampling strategies as well as shape parametrization guided sampling, which are applied to the geometric building blocks of the targeted visual representations. We will discuss the proposed sampling strategies, the derived illumination model, and demonstrate its capabilities when visualizing several dynamic molecules.     

Exploiting Distributed-Memory and Shared-Memory Parallelism on Clusters of SMPs with Data Parallel Programs

Clusters of SMPs are hybrid-parallel architectures that combine the main concepts of distributed-memory and shared-memory parallel machines. Although SMP clusters are widely used in the high performance computing community, there exists no single programming paradigm that allows exploiting the hierarchical structure of these machines. Most parallel applications deployed on SMP clusters are based on MPI, the standard API for distributed-memory parallel programming, and thus may miss a number of optimization opportunities offered by the shared memory available within SMP nodes. In this paper we present extensions to the data parallel programming language HPF and associated compilation techniques for optimizing HPF programs on clusters of SMPs. The proposed extensions enable programmers to control key aspects of distributed-memory and shared-memory parallelization at a high-level of abstraction. Based on these language extensions, a compiler can adopt a hybrid parallelization strategy which closely reflects the hierarchical structure of SMP clusters by automatically exploiting shared-memory parallelism based on OpenMP within cluster nodes and distributed-memory parallelism utilizing MPI across nodes. We describe the implementation of these features in the VFC compiler and present experimental results which show the effectiveness of these techniques.     

A distributed and hierarchical strategy for autonomic grid‐enabled cooperative metaheuristics with applications

The adoption of the same cluster‐based programming strategies for grid applications, although requiring minimal effort from a programmer's point of view, does not always take advantage of the available computational resources to their fullest extent. This paper investigates the impact of a distributed and hierarchical autonomic strategy on the performance of parallel metaheuristics to solve hard combinatorial optimization problems on grids. Two problems, the mirrored traveling tournament problem and the bounded diameter minimum spanning tree problem, for which high quality sequential heuristics based on the paradigms of the GRASP and Iterated Local Search metaheuristics already exist, are employed as case‐studies. The computational results obtained on a grid by the novel autonomic strategy show that outstanding performance improvements over the traditional master–worker parallelization approach can be achieved.     

Robust ℋ︁∞ PID control for multivariable networked control systems with disturbance/noise attenuation

In this paper, we study the design problem of PID controllers for networked control systems (NCSs) with polyhedral uncertainties. The load disturbance and measurement noise are both taken into account in the modeling to better reflect the practical scenario. By using a novel technique, the design problem of PID controllers is converted into a design problem of output feedback controllers. Our goal of this paper is two‐fold: (1) To design the robust PID tracking controllers for practical models; (2) To develop the robust ??_∞ PID control such that load and reference disturbances can be attenuated with a prescribed level. Sufficient conditions are derived by employing advanced techniques for achieving delay dependence. The proposed controller can be readily designed based on iterative suboptimal algorithms. Finally, four examples are presented to show the effectiveness of the proposed methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Parallelization methods for implementation of discharge simulation along resin insulator surfaces

In this paper, we will investigate the implementation of the parallelization approaches used in the program of discharge simulation along resin insulator surfaces in SF₆/N₂ gas mixture which initially consumes a great deal of computational time. In a general way, this simulation program spent 10 days of execution to achieve satisfactory research results. For this reason, the goal of our paper is to reduce the execution time by parallelizing this program. Three parallelization approaches were used in our simulation: (i) splitting by different types of the charged particles using a distributed-memory approach, (ii) splitting by physical domain using a distributed-memory approach, and (iii) splitting by both domain and charged particles using multi-level distributed and shared memory approach. At last, the three approaches are tested on a Linux cluster composed of six dual-core PCs, and the experimental results show that all the parallelization approaches achieve the goal of reducing the execution time to a certain extent. In addition, among these approaches, the multi-level approach offers the most effective parallelization method for implementing this simulation on symmetrical multi-processing (SMP) clusters.     

Fuzzy entropy based optimization of clusters for the segmentation of lungs in CT scanned images

In this paper, we have proposed a method for segmentation of lungs from Computed Tomography (CT)-scanned images using spatial Fuzzy C-Mean and morphological techniques known as Fuzzy Entropy and Morphology based Segmentation. To determine dynamic and adaptive optimal threshold, we have incorporated Fuzzy Entropy. We have proposed a novel histogram-based background removal operator. The proposed system is capable to perform fully automatic segmentation of CT Scan Lung images, based solely on information contained by the image itself. We have used different cluster validity functions to find out optimal number of clusters. The proposed system can be used as a basic building block for Computer-Aided Diagnosis. The technique was tested against the 25 datasets of different patients received from Aga Khan Medical University, Pakistan. The results confirm the validity of technique as well as enhanced performance.     

DYNAMIC ANALYSES AND ROBUST STEERING CONTROLLER DESIGN FOR AUTOMATED LANE GUIDANCE OF HEAVY‐DUTY VEHICLES

In this paper, we present various linear analyses of the linearized lateral dynamics of heavy‐duty vehicles (HDVs) (tractor‐semitrailer type), which include time domain, frequency domain and pole/zero analyses. These analyses are conducted to examine the vehicle response to the steering input subjected to variations of speed, road adhesion coefficient, cargo load in the trailer, and look‐ahead distance for the lateral deviation sensor. These parameters (uncertainties) have significant influence on vehicle dynamics. It has been shown that redefining the look‐ahead lateral error as the controlled output has a favorable impact on the lateral control problem. Based on these analyses, a robust steering controller using H_∞ loop‐shaping procedure is designed for a tractor semitrailer combination to follow the road center line on both curved and straight highway sections. The proposed controller ensures the robust performance under model uncertainties which include varying vehicle longitudinal speed, road adhesion coefficient, and cargo load in the trailer. The performance of the designed controller is evaluated by simulations and validated by experiments.     

KernelHive: a new workflow‐based framework for multilevel high performance computing using clusters and workstations with CPUs and GPUs

The paper presents a new open‐source framework called KernelHive for multilevel parallelization of computations among various clusters, cluster nodes, and finally, among both CPUs and GPUs for a particular application. An application is modeled as an acyclic directed graph with a possibility to run nodes in parallel and automatic expansion of nodes (called node unrolling) depending on the number of computation units available. A methodology is proposed for parallelization and mapping of an application to the environment that includes selection of devices using a chosen optimizer, selection of best grid configurations for compute devices, optimization of data partitioning and the execution. One of possibly many scheduling algorithms can be selected considering execution time, power consumption, and so on. An easy‐to‐use GUI is provided for modeling and monitoring with a repository of ready‐to‐use constructs and computational kernels. The methodology, execution times, and scalability have been demonstrated for a distributed and parallel password‐breaking example run in a heterogeneous environment with a cluster and servers with different numbers of nodes and both CPUs and GPUs. Additionally, performance of the framework has been compared with an MPI + OpenCL implementation using a parallel geospatial interpolation application employing up to 40 cluster nodes and 320 cores. Copyright © 2015 John Wiley & Sons, Ltd.     

TreeMatrix: A Hybrid Visualization of Compound Graphs

We present a hybrid visualization technique for compound graphs (i.e. networks with a hierarchical clustering defined on the nodes) that combines the use of adjacency matrices, node‐link and arc diagrams to show the graph, and also combines the use of nested inclusion and icicle diagrams to show the hierarchical clustering. The graph visualized with our technique may have edges that are weighted and/or directed. We first explore the design space of visualizations of compound graphs and present a taxonomy of hybrid visualization techniques. We then present our prototype, which allows clusters (i.e. subtrees) of nodes to be grouped into matrices or split apart using a radial menu. We also demonstrate how our prototype can be used in the software engineering domain, and compare it to the commercial matrix‐based visualization tool Lattix using a qualitative user study.     

A general metric and parallel framework for adaptive image fusion in clusters

This article is dedicated to techniques and theories of image fusion in automatic ways and addresses two issues—the parameter setting and quality assessment. Optimal parameters are in demand for specific applications or comparison between fusion methods because, as basic evidence, different parameters bring different fusion effects varying over a large range. In this paper, we propose a general framework of online parameter training to search optimal values that best suit input images. Furthermore, we optimized the compute‐intensive training process using parallelization and genetic algorithm, as well as patches extraction. We also propose a metric—spatial and spectral distortion—as the learning target. The spatial and spectral distortion is a fuzzy combination of mean potential energy measuring spatial distortion and Q4 measuring spectral distortion. Optimization validation on weighted Gram–Schmidt fusion indicated linear or superlinear acceleration ability, which proved that the proposed learning framework can speed up the learning process of image fusion to an acceptable time, and can thus be applied to high‐performance platforms to process large volumes of data. Copyright © 2013 John Wiley & Sons, Ltd.     

A software framework for fine grain parallelization of cellular models with OpenMP: Application to fire spread

We are dealing here with the parallelization of fire spreading simulations following detailed physical experiments. The proposal presented in this paper has been tested and evaluated in collaboration with physicists to meet their requirements in terms of both performance and precision. For this purpose, an object-oriented framework using two abstraction levels has been developed. A first level considers the simulation as a global phenomenon which evolves in space and time. A local level describes the phenomena occurring on elementary parts of the domain. In order to develop an extensible and modular architecture, the cellular automata paradigm, the DEVS discrete event system formalism and design patterns have been used. Simulation treatments are limited to a set of active elements to improve execution times. A new kind of model, called Active-DEVS is then specified. The model is computed with a fine grain parallelization very efficient for present day multi-core processors which are elementary units of modern computing clusters and computing grids. In this paper, the parallelization with Open MultiProcessing (OpenMP) standard directives on Symmetric MultiProcessing (SMP) architectures is discussed and the efficiency of the retained solution is studied.     

Volume rendering of DCT-based compressed 3D scalar data

The paper proposes a scheme to perform volume rendering from compressed scalar data. Instead of decompressing the entire data set before rendering, blocks of data are decompressed as needed. Discrete cosine transform based compression technique is used to illustrate the method. The data is partitioned into overlapping blocks to permit local rendering and allow easy parallelization. Compression by factor of 20 to 30 produces rendering virtually indistinguishable from rendering using the original uncompressed data. Speedup is obtained by making use of spatial homogeneity detected in the transform domain. Rendering time using the proposed approach is less than that of direct rendering from the entire uncompressed data. The proposed method thus offers an attractive option to reduce storage, computation, and transmission overhead of otherwise huge data sets