Optimization of minimum volume constrained hyperspectral image unmixing on CPU–GPU heterogeneous platform

Hyperspectral unmixing is essential for efficient hyperspectral image processing. Nonnegative matrix factorization based on minimum volume constraint (MVC-NMF) is one of the most widely used methods for unsupervised unmixing for hyperspectral image without the pure-pixel assumption. But the model of MVC-NMF is unstable, and the traditional solution based on projected gradient algorithm (PG-MVC-NMF) converges slowly with low accuracy. In this paper, a novel parallel method is proposed for minimum volume constrained hyperspectral image unmixing on CPU–GPU Heterogeneous Platform. First, a optimized unmixing model of minimum logarithmic volume regularized NMF is introduced and solved based on the second-order approximation of function and alternating direction method of multipliers (SO-MVC-NMF). Then, the parallel algorithm for optimized MVC-NMF (PO-MVC-NMF) is proposed based on the CPU–GPU heterogeneous platform, taking advantage of the parallel processing capabilities of GPUs and logic control abilities of CPUs. Experimental results based on both simulated and real hyperspectral images indicate that the proposed algorithm is more accurate and robust than the traditional PG-MVC-NMF, and the total speedup of PO-MVC-NMF compared to PG-MVC-NMF is over 50 times.     

Rematch: a highly reliable scheduling algorithm on heterogeneous wireless mesh network

In highly dynamic and heterogeneous wireless mesh networks (WMN), link quality will seriously affect network performance. Two challenges hinder us from achieving a highly efficient WMN. One is the channel dynamics. As in real network deployment, channel qualities are changing over time, which would seriously affect network bandwidth and reliability. Existing works are limited to the assumption that link quality values are fixed, and optimal scheduling algorithms are working on the fixed values, which would inevitably suffer from the link quality dynamics. Another challenge is the channel diversity. In single channel wireless networks, channel assignment and scheduling are NP\mathcal{NP} -hard. And in multichannel wireless networks, it could be even harder for higher throughput and efficient scheduling. In this study, we firstly characterize the stochastic behavior on wireless communications in a Markov process, which is based on statistical methodology. Secondly, on exploiting the stochastic behavior on wireless channels, we propose a stochastic programming model in achieving maximized network utilization. Considering the NP\mathcal{NP} -hardness, we propose a heuristic solution for it. The key idea in the proposed algorithm is a two-stage matching process named “Rematch.” Indeed, our solution to the stochastic network scheduling is a cross-layer approach. Also, we have proved that it is 2-approximate to the optimal result. Moreover, extensive simulations have been done, showing the efficiency of “Rematch” in highly dynamic and distributed wireless mesh networks.     

A scheduling algorithm for maximum throughput based on the link condition in heterogeneous network

Nowadays, a lot of wireless interfaces can be used by mobile users to access the Internet, such as WLAN, WiMAX, WlFI and even 3G. If a mobile terminal is equipped with multiple interfaces, it can use them simultaneously to improve the performance at the hot point where different RANs （Radio Access Networks） overlap. This paper proposes a scheduling algorithm based on the link condition that ensures the whole network has the maximum throughput. Simulation is also done to show the improvement of throughput with this scheduling algorithm.     

A particle swarm optimization and min–max-based workflow scheduling algorithm with QoS satisfaction for service-oriented grids

In service-orientated grids (SOG) environments, grid workflow schedulers play a critical role in providing quality-of-service (QoS) satisfaction for various end users (EUs) with diverse QoS objectives and optimization requirements. The EU requirements are not only many and conflicting, but also involve constraints of various degrees—loose, moderate or tight. However, most of the existing scheduling approaches violate EU constraints in tight situations and suffer inferior QoS optimization results. In this paper, a constraints-aware multi-QoS workflow scheduling strategy is proposed based on particle swarm optimization (PSO) and a proposed look-ahead heuristic (LAPSO) to improve performance in such situations. The algorithm selects the best scheduling solutions based on the proposed constraint-handling strategy. It hybridises PSO with a novel look-ahead mechanism based on a min–max heuristic, which deterministically improves the quality of the best solutions. Extensive simulation experiments have been carried out to evaluate the performance of the proposed approach. The simulation results show that the LAPSO algorithm guarantees satisfaction (0% violation) of the EU constraints even in tight situations. It also outperforms the comparison algorithm, with about 30% increase, in terms of cumulative QoS satisfaction of optimization requirements. In addition, the new scheme significantly reduces the CPU time by about 75% compared to the benchmark algorithm.     

Hyperspectral anomaly detection based on constrained eigenvalue–eigenvector model

Anomaly detection in a large area using hyperspectral imaging is an important application in real-time remote sensing. Anomaly detectors based on subspace models are suitable for such an anomaly and usually assume the main background subspace and its dimensions are known. These detectors can detect the anomaly for a range of values of the dimension of the subspace. The objective of this paper is to develop an anomaly detector that extends this range of values by assuming main background subspace with an unknown user-specified dimension and by imposing covariance of error to be a diagonal matrix. A pixel from the image is modeled as the sum of a linear combination of the unknown main background subspace and an unknown error. By having more unknown quantities, there are more degrees of freedom to find a better way to fit data to the model. By having a diagonal matrix for the covariance of the error, the error components become uncorrelated. The coefficients of the linear combination are unknown, but are solved by using a maximum likelihood estimation. Experimental results using real hyperspectral images show that the anomaly detector can detect the anomaly for a significantly larger range of values for the dimension of the subspace than conventional anomaly detectors.     

Resource constrained project scheduling problem with discounted earliness–tardiness penalties: Mathematical modeling and solving procedure

In this paper, we consider the resource-constrained project scheduling problem with a due date for each activity. The objective is to minimize the net present value of the earliness–tardiness penalty costs. The problem is first mathematically modeled. Then, two meta-heuristics, genetic algorithm and simulated annealing are proposed to solve this strongly NP-hard problem. Design of experiments and response surface methodology are employed to fine-tune the meta-heuristics’ parameters. Finally, a comprehensive computational experiment is described, performed on a set of instances and the results are analyzed and discussed.     

A spatio-temporal constrained hierarchical scheduling strategy for multiple warehouse mobile robots under industrial cyber–physical system

The industrial cyber–physical system (ICPS) framework can reduce the computational load and improve task efficiency. This paper studies the ICPS-based scheduling strategy for multi-warehouse mobile robots (multi-WMRs). First of all, the possible congestion problem is considered in topological map modeling, which is transformed into a new path time cost index. Second, each robot independently executes the path planning algorithm, which realizes distributed path computation and takes time cost and destination distance into account. The improved task assignment strategy includes task evaluation and decision-making, which are considered part of the planning and help to improve task efficiency. Finally, the complete scheduling process is applied to the novel ICPS architecture, including cost evaluation, path planning, task assignment, and collision avoidance. In numerical experiments, the task efficiency has been increased by 24.8% to recent research and 14.59% to previous work. The average congestion time is reduced by 28.41%, and the planning time is reduced to 10.13% of the traditional method.     

Efficient application scheduling in mobile cloud computing based on MAX–MIN ant system

Combining the advantages of mobile computing and cloud computing, Mobile Cloud Computing (MCC) greatly enriches the types of applications on mobile devices and enhances the quality of service of the applications. Under various circumstances, researchers have put forward several MCC architectures. However, it still remains a challenging task of how to design a reasonable mobile cloud model with efficient application processing structure for some particular environment. This paper firstly presents a Hybrid Local Mobile Cloud Model (HLMCM) with detailed application scheduling structure. Secondly, a scheduling algorithm for HLMCM based on MAX–MIN Ant System is put forward. Finally, the effectiveness and suitability of our proposed algorithms are evaluated through a series of simulation experiments.     

Earliness–tardiness minimization on scheduling a batch processing machine with non-identical job sizes

This paper considers the scheduling problem of minimizing earliness–tardiness (E/T) on a single batch processing machine with a common due date. The problem is extended to the environment of non-identical job sizes. First, a mathematical model is formulated, which is tested effectively under IBM ILOG CPLEX using the constraint programming solver. Then several optimal properties are given to schedule batches effectively, and by introducing the concept of ARB (Attribute Ratio of Batch), it is proven that the ARB of each batch should be made as small as possible in order to minimize the objective, designed as the heuristic information for assigning jobs into batches. Based on these properties, a heuristic algorithm MARB (Minimum Attribute Ratio of Batch) for batch forming is proposed, and a hybrid genetic algorithm is developed for the problem under study by combining GA (genetic algorithm) with MARB. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in the literature, both for small and large problem instances.     

Priority-based joint EDF–RM scheduling algorithm for individual real-time task on distributed systems

The Journal of Supercomputing - Multiple tasks arrive in the distributed systems that can be executed in either parallel or sequential manner. Before the execution, tasks are scheduled prioritywise...     

A constrained optimization perspective on actor–critic algorithms and application to network routing

We propose a novel actor–critic algorithm with guaranteed convergence to an optimal policy for a discounted reward Markov decision process. The actor incorporates a descent direction that is motivated by the solution of a certain non-linear optimization problem. We also discuss an extension to incorporate function approximation and demonstrate the practicality of our algorithms on a network routing application.     

Performance modeling and analysis of heterogeneous lattice Boltzmann simulations on CPU–GPU clusters

Computational fluid dynamic simulations are in general very compute intensive. Only by parallel simulations on modern supercomputers the computational demands of complex simulation tasks can be satisfied. Facing these computational demands GPUs offer high performance, as they provide the high floating point performance and memory to processor chip bandwidth. To successfully utilize GPU clusters for the daily business of a large community, usable software frameworks must be established on these clusters. The development of such software frameworks is only feasible with maintainable software designs that consider performance as a design objective right from the start. For this work we extend the software design concepts to achieve more efficient and highly scalable multi-GPU parallelization within our software framework waLBerla for multi-physics simulations centered around the lattice Boltzmann method. Our software designs now also support a pure-MPI and a hybrid parallelization approach capable of heterogeneous simulations using CPUs and GPUs in parallel. For the first time weak and strong scaling performance results obtained on the Tsubame 2.0 cluster for more than 1000 GPUs are presented using waLBerla. With the help of a new communication model the parallel efficiency of our implementation is investigated and analyzed in a detailed and structured performance analysis. The suitability of the waLBerla framework for production runs on large GPU clusters is demonstrated. As one possible application we show results of strong scaling experiments for flows through a porous medium.     

A parallel solving method for block-tridiagonal equations on CPU–GPU heterogeneous computing systems

Solving block-tridiagonal systems is one of the key issues in numerical simulations of many scientific and engineering problems. Non-zero elements are mainly concentrated in the blocks on the main diagonal for most block-tridiagonal matrices, and the blocks above and below the main diagonal have little non-zero elements. Therefore, we present a solving method which mixes direct and iterative methods. In our method, the submatrices on the main diagonal are solved by the direct methods in the iteration processes. Because the approximate solutions obtained by the direct methods are closer to the exact solutions, the convergence speed of solving the block-tridiagonal system of linear equations can be improved. Some direct methods have good performance in solving small-scale equations, and the sub-equations can be solved in parallel. We present an improved algorithm to solve the sub-equations by thread blocks on GPU, and the intermediate data are stored in shared memory, so as to significantly reduce the latency of memory access. Furthermore, we analyze cloud resources scheduling model and obtain ten block-tridiagonal matrices which are produced by the simulation of the cloud-computing system. The computing performance of solving these block-tridiagonal systems of linear equations can be improved using our method.     

A heterogeneous parallel Red–Black SOR technique and the numerical study on SIMPLE

The Journal of Supercomputing - A basic heterogeneous parallel Red–Black successive over-relaxation (SOR) implement, the mono-color floating-point scheme, was developed on graphics processing...     

A novel hybrid PSO–GA meta-heuristic for scheduling of DAG with communication on multiprocessor systems

This work presents a novel hybrid meta-heuristic that combines particle swarm optimization and genetic algorithm (PSO–GA) for the job/tasks in the form of directed acyclic graph (DAG) exhibiting inter-task communication. The proposed meta-heuristic starts with PSO and enters into GA when local best result from PSO is obtained. Thus, the proposed PSO–GA meta-heuristic is different than other such hybrid meta-heuristics as it aims at improving the solution obtained by PSO using GA. In the proposed meta-heuristic, PSO is used to provide diversification while GA is used to provide intensification. The PSO–GA is tested for task scheduling on two standard well-known linear algebra problems: LU decomposition and Gauss–Jordan elimination. It is also compared with other states-of-the-art heuristics for known solutions. Furthermore, its effectiveness is evaluated on few large sizes of random task graphs. Comparative study of the proposed PSO-GA with other heuristics depicts that the PSO–GA performs quite effectively for multiprocessor DAG scheduling problem.     

A software-based dynamic-warp scheduling approach for load-balancing the Viola–Jones face detection algorithm on GPUs

Face detection is a key component in applications such as security surveillance and human–computer interaction systems, and real-time recognition is essential in many scenarios. The Viola–Jones algorithm is an attractive means of meeting the real time requirement, and has been widely implemented on custom hardware, FPGAs and GPUs. We demonstrate a GPU implementation that achieves competitive performance, but with low development costs. Our solution treats the irregularity inherent to the algorithm using a novel dynamic warp scheduling approach that eliminates thread divergence. This new scheme also employs a thread pool mechanism, which significantly alleviates the cost of creating, switching, and terminating threads. Compared to static thread scheduling, our dynamic warp scheduling approach reduces the execution time by a factor of 3. To maximize detection throughput, we also run on multiple GPUs, realizing 95.6 FPS on 5 Fermi GPUs.     

A biologically constrained architecture for developmental learning of eye–head gaze control on a humanoid robot

In this paper we describe a biologically constrained architecture for developmental learning of eye–head gaze control on an iCub robot. In contrast to other computational implementations, the developmental approach aims to acquire sensorimotor competence through growth processes modelled on data and theory from infant psychology. Constraints help shape learning in infancy by limiting the complexity of interactions between the body and environment, and we use this idea to produce efficient, effective learning in autonomous robots. Our architecture is based on current thinking surrounding the gaze mechanism, and experimentally derived models of stereotypical eye–head gaze contributions. It is built using our proven constraint-based field-mapping approach. We identify stages in the development of infant gaze control, and propose a framework of artificial constraints to shape learning on the robot in a similar manner. We demonstrate the impact these constraints have on learning, and the resulting ability of the robot to make controlled gaze shifts.     

A novel heterogeneous algorithm to simulate multiphase flow in porous media on multicore CPU–GPU systems

Multiphase flow implementations of the lattice Boltzmann method (LBM) are widely applied to the study of porous medium systems. In this work, we construct a new variant of the popular “color” LBM for two-phase flow in which a three-dimensional, 19-velocity (D3Q19) lattice is used to compute the momentum transport solution while a three-dimensional, seven velocity (D3Q7) lattice is used to compute the mass transport solution. Based on this formulation, we implement a novel heterogeneous GPU-accelerated algorithm in which the mass transport solution is computed by multiple shared memory CPU cores programmed using OpenMP while a concurrent solution of the momentum transport is performed using a GPU. The heterogeneous solution is demonstrated to provide speedup of 2.6×$2.6\times$ as compared to multi-core CPU solution and 1.8×$1.8\times$ compared to GPU solution due to concurrent utilization of both CPU and GPU bandwidths. Furthermore, we verify that the proposed formulation provides an accurate physical representation of multiphase flow processes and demonstrate that the approach can be applied to perform heterogeneous simulations of two-phase flow in porous media using a typical GPU-accelerated workstation.     

Comments on “Analysis of non-linear radiative stagnation point flow of Carreau fluid with homogeneous–heterogeneous reactions”

Microsystem Technologies - These commentaries show that the heat generation/absorption parameter (γ1) is dimensionless only if the parameter (m) = 1, which means constant surface...