ALBERT

Beschreibung: The multiple sequence alignment (MSA) problem has become relevant to several areas in bioinformatics from finding sequences family, detecting structural homologies of protein/DNA sequences, determining functions of protein/DNA sequences to predict patients diseases by comparing DNAs of patients in disease discovery, etc. The MSA is a NP-hard problem. In this paper, two new methods based on a cultural algorithm, namely the method of musical composition, for the solution of the MSA problem are introduced. The performance of the first and second versions were evaluated and analyzed on 26 and 12 different benchmark alignments, respectively. Test instances were taken from BAliBASE 3.0. Alignment accuracies are computed using the QSCORE program, which is a quality scoring program that compares two multiple sequence alignments. Numerical results on the tackled instances indicate that the performance levels of the proposed versions of the MMC are promising. In particular, the experimental results show that the second version found the best alignment reported in the specialized literature in 25  \(\%\) of the tested instances. Besides, for 50  \(\%\) of the tested instances, the second version achieved the second best alignment. Finally, the significance of the numerical results were analyzed according to the Wilcoxon rank-sum test, which indicated that the second proposed version is statistically similar to some state-of-the-art techniques for the MSA problem.

Beschreibung: When optimizing performance on a GPU, control flow divergence of threads in one warp can make up the possible performance bottlenecks. In our hand-coded GPU stencil computation optimization, with a view to remove this control flow divergence brought by conventional mapping method between global memory and shared memory, we devise a new mapping mechanism by modeling the coalesced memory accesses of GPU threads and the aligned ghost zone overheads to remove conditional statements of the boundary XY-tile stencil computation points for improved performance. In addition, we utilize only one XY-tile loaded into registers in every stencil computation iteration, common sub-expression elimination and software prefetching to reduce overheads. Finally, detailed performance evaluation demonstrates that global memory access traffic is close to the idealized lower bound value through our optimized policies, that is to say, in every computed point of one XY-tile the memory access traffic is roughly 6 and 4 % more than 8 bytes per XY-tile point of the idealized lower bound memory access traffic in which ghost zone overheads are not taken into consideration on Tesla C2050 and Kepler K20X respectively.

Beschreibung: Cloud Computing introduces a novel computing paradigm that allows the users to run their applications on a customized environment using on-demand resources. This novel computing concept is enabled by several technologies including the Web, virtualization, distributed file systems as well as parallel programming models. For parallel computing on the Cloud, MapReduce is currently the first choice for Cloud providers to deliver data analysis services because this model is specially designed for data-intensive applications while a Cloud centre is actually also a data centre hosting a huge amount of data usually in Petascale. The current deployment of MapReduce on the Cloud, however, follows the traditional execution model of MapReduce that needs the support of a cluster manager. This means that the single virtual machines created on the Cloud have to be organized into a cluster in order to be capable of running a MapReduce application. This is not only a burden for system management but also prohibits inter-Cloud computing that can involve the resources of different Clouds to solve large problems with big data or distributed data. We developed a software framework for individual virtual machines to execute a MapReduce application in a parallel/collaborative way without the necessity of installing a middleware or specific software package for system management. A focus of this research work is a Single-Sign-On (SSON) mechanism that enables the remote access to the individual machines. We validated the SSON mechanism together with the entire MapReduce framework using a private Cloud. Experimental results show both the functionality and the feasibility of our approach.

Beschreibung: Multi-policy resource management have been considered as an efficient methodology for delivering ready-to-use media-optimized applications in Software-Defined Networks (SDNs). Prioritized flow scheduling ensures high-speed communication in SDNs under large-scale distribution, heterogeneity of network resources, and exponential distribution of the flows granularity. The effectiveness of priority-based approaches depends usually on the control mechanism of the resource management. In this paper we improve the resource utilization by developing a novel adaptive scheduling strategy. We came with an effecting scheduling strategy to determine what resource to be allocated to a set of flows keeping their priority, increasing the average utilization of resources and, most importantly, establishing a virtual circuit for a specific flow over a network. Our theoretical remarks and extensive simulation results show that the proposed scheduling strategies can achieve the described goals.

Beschreibung: In the past, people have focused on cluster computing and grid computing. Now, however, this focus has shifted to cloud computing. Irrespective of what techniques are used, there are always storage requirements. The challenge people face in this area is the huge amount of data to be stored, and its complexity. People are now using many cloud applications. As a result, service providers must serve increasingly more people, causing more and more connections involving substantially more data. These problems could have been solved in the past, but in the age of cloud computing, they have become more complex. This paper focuses on cloud computing infrastructure, and especially data services. The goal of this paper is to implement a high performance and load balancing, and able-to-be-replicated system that provides data storage for private cloud users through a virtualization system. This system extends and enhances the functionality of the Hadoop distributed system. The proposed approach also implements a resource monitor of machine status factors such as CPU, memory, and network usage to help optimize the virtualization system and data storage system. To prove and extend the usability of this design, a synchronize app was also developed running on Android based on our distributed data storage.

Beschreibung: In this paper, a graphics processor unit (GPU) accelerated particle filtering algorithm is presented with an introduction to a novel resampling technique. The aim remains in the mitigation of particle impoverishment as well as computational burden, problems which are commonly associated with classical (systematic) resampled particle filtering. The proposed algorithm employs a priori-space dependent distribution in addition to the likelihood, and hence is christened as dual distribution dependent (D3) resampling method. Simulation results exhibit lesser values for root mean square error (RMSE) in comparison to that for systematic resampling. D3 resampling is shown to improve particle diversity after each iteration, thereby affecting the overall quality of estimation. However, computational burden is significantly increased owing to few excessive computations within the newly formulated resampling framework. With a view to obtaining parallel speedup we introduce a CUDA version of the proposed method for necessary acceleration by GPU. The GPU programming model is detailed in the context of this paper. Implementation issues are discussed along with illustration of empirical computational efficiency, as obtained by executing the CUDA code on Quadro 2000 GPU. The GPU enabled code has a speedup of 3 and 4 over the sequential executions of systematic and D3 resampling methods respectively. Performance both in terms of RMSE and running time have been elaborated with respect to different selections for threads per block towards effective implementations. It is in this context that, we further introduce a cost to performance metric (CPM) for assessing the algorithmic efficiency of the estimator, involving both quality of estimation and running time as comparative factors, transformed into a unified parameter for assessment. CPM values for estimators obtained from all such different choices for threads per block have been determined and a final value for the chosen parameter is resolved for generation of a holistic effective estimator.

Beschreibung: The combination of vehicles and wireless communication has resulted in a promising future for vehicular ad hoc networks (VANETs). The VANET is a kind of network that can be built randomly, quickly and temporarily without any infrastructure. The routing of data in a VANET is a challenging task due to the high dynamics, unstable connection environments and transfer direction limit (real road planning) involved. However, it has been discussed that radio obstacles, as found in urban areas, have a significant negative impact on the performance of location based routing. Therefore, the design of a suitable routing protocol for VANETs is an important issue, especially in regard to inter-vehicle communication applications. This paper proposes a energy-efficient geographic routing algorithm that uses the direction, density and distance between nodes in the crossroad routing strategy, to improve the link stability. We compare the novel scheme, ad hoc on-demand distance vector, and dynamic source routing (DSR) for packet loss rate and average end-to-end delay in VANETs to reduce the power consumptions. The simulation results are obtained which demonstrate the power effectiveness of our proposed routing strategy.

Beschreibung: Remote sensing image processing is characterized with features of massive data processing, intensive computation, and complex processing algorithms. These characteristics make the rapid processing of remote sensing images very difficult and inefficient. The rapid development of general-purpose graphic process unit (GPGPU) computing technology has resulted in continuous improvement in GPU computing performance. Its strong floating point calculating capability, high intensive computation, small volume, and excellent performance-cost ratio provide an effective solution to the problems faced in remote sensing image processing. However, current usage of GPU in remote sensing image processing applications has been limited to specific parallel algorithms and their optimization of processes, rather than formed well-established models and methods. This has introduced serious problems to the development of remote sensing image processing algorithms on GPU architectures. For example, GPU parallel strategies and algorithms are highly coupled and non-reusable. The processing system is closely associated with the GPU hardware so that programming for remote sensing algorithms on GPU is nothing but easy. In this paper, we attempt to explore a reusable GPU-based remote sensing image parallel processing model and to establish a set of parallel programming templates, which provides programmers with a more simple and effective way for programming parallel remote sensing image processing algorithms.