Accelerating Needleman-Wunsch global alignment algorithm with GPUs

According to most researches it is stated that 1-3% of the human body mass consist of microbiota. The gut and intestinal part of human has some several types of microorganisms, which is important for human health and diseases. Hence, understanding the behavior of the human gut and intestine microbiomes increase the chance of detecting and predicting the disease earlier to take the precautions for treatment. Time is an important measure for collecting more information about gut and intestine microbiota so, the proposed work used the 16S rRNA metagenomic approach which is a best suited approach that provides a knowledge-based way to understand the human microbiota much faster. The nucleotide database of bacterial 16S rRNA gene sequences isolated from human intestinal and fecal samples used to develop microbiota microarray that's includes Human Intestine Microbiomes, their Protein's Information and the weight of each protein in the dataset that's calculated used two efficient techniques such as KMeans Clustering Algorithm and Needleman-Wunsch Algorithm. This proposed work contribution highlights on avoiding time consumption of Needleman-Wunsch sequence alignment Algorithm on assigning weights to such large scale of proteins that counts 56117 Protein. In this work validation experiments, the microarray correctly identified genomic DNA from all 18bacterial species used. According to the analytical study of this approach on the dataset it proves that calculating the alignment distance for large amount of sequences become more efficient and faster when extracting some features that is considered an important factor in clustering the dataset into 8 clusters which reduce the runtime of full dataset from 2 years to 3 days. This microbiota microarrays will be clustered using Genetic algorithm taking into consideration the protein weight assigned by Needleman-Wunsch Algorithm to grouping the human intestine microbiomes' proteins to k clusters to get identity for proteins that has unknown structure and get the interaction between all proteins using Protein-Protein Interaction Model.

Neural networks have proved to be effective in solving a wide range of problems. As problems become more and more demanding, they require larger neural networks, and the time used for learning is consequently greater. Parallel implementations of learning algorithms are therefore vital for a useful application. Implementation, however, strongly depends on the features of the learning algorithm and the underlying hardware architecture. For this experimental work a dynamic problem was chosen which implicates the use of recurrent neural networks and a learning algorithm based on the paradigm of learning automata. Two parallel implementations of the algorithm were applied - one on a computing cluster using MPI and OpenMP libraries and one on a graphics processing unit using the CUDA library. The performance of both parallel implementations justifies the development of parallel algorithms.

Several problems in computational biology require the all-against-all pairwise comparisons of tens of thousands of individual biological sequences. Each such comparison can be performed with the well-known Needleman-Wunsch alignment algorithm. However, with the rapid growth of biological databases, performing all possible comparisons with this algorithm in serial becomes extremely time-consuming. The massive computational power of graphics processing units (GPUs) makes them an appealing choice for accelerating these computations. As such, CPU-GPU clusters can enable all-against-all comparisons on large datasets.

Principal component analysis (PCA) is a key statistical technique for multivariate data analysis. For large data sets, the common approach to PCA computation is based on the standard NIPALS-PCA algorithm, which unfortunately suffers from loss of orthogonality, and therefore its applicability is usually limited to the estimation of the first few components. Here we present an algorithm based on Gram-Schmidt orthogonalization (called GS-PCA), which eliminates this shortcoming of NIPALS-PCA. Also, we discuss the GPU (Graphics Processing Unit) parallel implementation of both NIPALS-PCA and GS-PCA algorithms. The numerical results show that the GPU parallel optimized versions, based on CUBLAS (NVIDIA), are substantially faster (up to 12 times) than the CPU optimized versions based on CBLAS (GNU Scientific Library).

This paper presents the design and implementation of the computationally intensive Smith Waterman and Needleman-Wunsch algorithms on the Virtex-5 (XC5VLX110) FPGA (Field Programmable Gate Array). A parameterisable architecture of the processing element (PE) for the pairwise biological sequence analysis is presented. We evaluate our core's performance in terms of speed up performance and normalised cell update per second (CUPS) against various computing platforms including our previous Graphics Processor Unit (GPU) and other FPGA implementations. Our efficient hardware design produces the highest normalised throughput ever reported in literature.

Graphic processing units (GPUs) emerged recently as an exciting new hardware environment for a truly parallel implementation and execution of Nature and Bio-inspired Algorithms with excellent price-to-power ratio. In contrast to common multicore CPUs that contain up to tens of independent cores, the GPUs represent a massively parallel single-instruction multiple-data devices that can nowadays reach peak performance of hundreds and thousands of giga floating-point operations per second. Nature and Bio-inspired Algorithms implement parallel optimization strategies in which a single candidate solution, a group of candidate solutions (population), or multiple populations seek for optimal solution or set of solutions of given problem. Genetic algorithms (GA) constitute a family of traditional and very well-known nature-inspired populational meta-heuristic algorithms that have proved its usefulness on a plethora of tasks through the years. Differential evolution (DE) is another efficient populational meta-heuristic algorithm for real-parameter optimization. Particle swarm optimization (PSO) can be seen as nature-inspired multiagent method in which the interaction of simple independent agents yields intelligent collective behavior. Simulated annealing (SA) is global optimization algorithm which combines statistical mechanics and combinatorial optimization with inspiration in metallurgy. This survey provides a brief overview of the latest state-of-the-art research on the design, implementation, and applications of parallel GA, DE, PSO, and SA-based methods on the GPUs.

Efficient biological sequence (proteins or DNA) alignment is an important and challenging task in bioinformatics. It is similar to string matching in the context of biological data and is used to infer the evolutionary relationship between a set of protein or DNA sequences. An accurate alignment can provide valuable information for experimentation on the newly found sequences. It is indispensable in basic research as well as in practical applications such as pharmaceutical development, drug discovery, disease prevention and criminal forensics. Many algorithms and methods, such as, dot plot (Gibbs & McIntyre, 1970),Needleman-Wunsch (N-W) (Needleman & Wunsch, 1970), Smith-Waterman (S-W) (Smith & Waterman, 1981), FASTA (Pearson & Lipman, 1985), BLAST (Altschul et al., 1990), HMMER (Eddy, 1998) and ClustalW (Thompson et al., 1994) have been proposed to perform and accelerate sequence alignment activities. An overview of these methods is given in (Hasan et al., 2007). Out of these, S-W algorithm is an optimal sequence alignment method, but its computational cost makes it inappropriate for practical purposes. To develop efficient and optimal sequence alignment solutions, the S-W algorithm has recently been implemented on emerging accelerator platforms such as Field Programmable Gate Arrays (FPGAs), Cell Broadband Engine (Cell/B.E.) and Graphics Processing Units (GPUs) (Buyukkur & Najjar, 2008; Hasan et al., 2010; Liu et al., 2009; 2010; Lu et al., 2008). This chapter aims at providing a broad overview of sequence alignment in general with particular emphasis on the classification and discussion of available methods and their comparison. Further, it reviews in detail the acceleration approaches based on implementations on different platforms and provides a comparison considering different parameters. This chapter is organized as follows: The remainder of this section gives a classification, discussion and comparison of the available methods and their hardware acceleration. Section 2 introduces the S-W algorithm which is the focus of discussion in the succeeding sections. Section 3 reviews CPU-based acceleration. Section 4 provides a review of FPGA-based acceleration. Section 5 overviews GPU-based acceleration. Section 6 presents a comparison of accelerations on different platforms, whereas Section 7 concludes the chapter.

Routing plays a major role in VANETs by helping a vehicle to reach the destination by finding an optimal path. These routing decisions are affected by the congestion on roads. Several approaches have been proposed to improve this problem of handling congestion thorough various traffic management strategies. ACO is being used in literature to provide routing in real time environment. Modified Ant Colony Optimization (MACO) algorithm is used to reduce the travel time of the journey by avoiding congested routes. This paper proposes a parallel implementation for MACO algorithm in order to further reduce the travel time due to faster computation using GPUs for the vehicles on move. Parallel implementation is done using parallel architecture on the Graphics Processing Unit (GPU) at NVIDIA GeForce 710 M using C language running CUDA (Compute Unified Device Architecture) toolkit 7.0 on Microsoft Visual Studio 2010. The obtained results for proposed parallel MACO when compared with the parallel implementation of the standard Dijkstra algorithm and that of the existing MACO algorithm on a real world North-West Delhi map with an increased number of vehicles significantly reduce the travel time.

The Graphical Processing Unit (GPU) has become a competitive general purpose computational hardware platform in the last few years. Recent improvements in GPUs highly parallel programming capabilities such as Compute Unified Device Architecture(CUDA) has lead to a variety of complex applications with tremendous performance improvements. Genetic Sequence alignment is considered to be one of the application domains which require further improvements in the execution speed, because it is a computationally intensive task with increased database size. We focus on using the massively parallel architecture of GPU as a solution for the improvement of sequence alignment task. For that purpose we have implemented a CUDA based heterogeneous solution for the global sequence alignment task with Needleman-Wunsch dynamic programming algorithm. We have compared different levels of memory access patterns to identify better parallelization strategy with different ways of kernel access and thread utilization methods.

In scientific computing, solving systems of linear equations is a very essential and widely used complex problem. Finding an optimal solution, using various algorithms, to these highly computation problems is very important. As the number of variables increases, the complexity of these problems also increases. These huge amounts of computations require High Performance Computing (HPC) systems, where efficient task distribution and accurate performance analysis are highly required on using these systems. In this paper, the computation performance using multicore architecture and Graphics Processing Units (GPUs) are compared with an analysis and expectations for the future of the new multicore systems. The number of cores in GPUs is extremely larger compared to the multicore architecture, while the memory overhead degrades the performance of GPUs dramatically. The expected scalability of the multicore system architecture tends to have better performance than GPUs.

Sparse matrices are involved in linear systems, eigensystems and partial differential equations from a wide spectrum of scientific and engineering disciplines. Hence, sparse matrix vector product (SpMV) is considered as key operation in engineering and scientific computing. For these applications the optimization of the sparse matrix vector product (SpMV) is very relevant. However, the irregular computation involved in SpMV prevents the optimum exploitation of computational architectures when the sparse matrices are very large. Graphics Processing Units (GPUs) have recently emerged as platforms that yield outstanding acceleration factors. SpMV implementations for GPUs have already appeared on the scene. This work proposes and evaluates new implementations of SpMV for GPUs called ELLR-T. They are based on the format ELLPACK-R, which allows storage of the sparse matrix in a regular manner. A comparative evaluation against a variety of storage formats previously proposed has been carried out based on a representative set of test matrices. The results show that: (1) the SpMV is highly accelerated with GPUs; (2) the performance strongly depends on the specific pattern of the matrix; and (3) the implementations ELLR-T achieve higher overall performance. Consequently, the new implementations of SpMV, ELLR-T, described in this paper can help to exploit the GPUs, because, they achieve high performance and they can be easily joined in the engineering and scientific computing.

Electric vehicles (EVs) are becoming more and more popular as they provide significant environmental benefits compared to fossil-fuel vehicles. However, they represent substantial loads on the power grid, and the scheduling of EV charging can be a challenge, especially in large parking lots. This paper presents a metaheuristic-based approach parallelized on multicore processors (CPU) and graphics processing units (GPU) to optimize the scheduling of EV charging in a single smart parking lot. The proposed method uses a particle swarm optimization algorithm that takes as input the arrival time, the departure time, and the power demand of the vehicles and produces an optimized charging schedule for all vehicles in the parking lot, which minimizes the overall charging cost while respecting the chargers’ capacity and the parking lot feeder capacity. The algorithm exploits task-level parallelism for the multicore CPU implementation and data-level parallelism for the GPU implementation. The proposed algorithm is tested in simulation on parking lots containing 20 to 500 EVs. The parallel implementation on CPUs provides a speedup of 7.1x, while the implementation on a GPU provides a speedup of up to 247.6x. The parallel implementation on a GPU is able to optimize the charging schedule for a 20-EV parking lot in 0.87 s and a 500-EV lot in just under 30 s. These runtimes allow for real-time computation when a vehicle arrives at the parking lot or when the electricity cost profile changes.

Based on the principle of QR loop iterations, this paper implements a parallel algorithm based on the hardware of GPU (Graphic Process Unit) by using routines from CUDA (Computer Unified Device Architecture) to find the eigenvalues of general matrices. CPU and GPU computing card form a client-server computing framework. Here, CPU can be regarded as a client and GPU card is considered as a computing server. For the experiment environment, this paper chooses a GPU card with the model of NVIDIA GeForce GTX460 as a server side and a CPU with the model of Intel Core i5-760 quad-core as a client side. Win7 64-bit is selected as the operating system. The parallel implementation consists of two parts:PA H and PA QR. PA H is a procedure that transforms a matrix A into the Hessenberg matrix B. PA QR is the actual parallel algorithm of the QR iterations that is imposed on the Hessenberg matrix B for finding eigenvalues of the general matrix A. The speedup ratio of the proposed algorithm is jarless when the number of iterations becomes greater. The experimental results show that the parallel implementation with CUDA on GPU only makes use of less running time than traditional sequential algorithms. The speedup ratio of PA H is between 1.79 and 7.81. The speedup ratio of PA QR is between 3.24 and 118.9. Especially, when the order of general matrix equals 8192, the amount of iterations becomes 10000. The speedup ratio of the PA-H and the PA QR can run up to 7.81 and 118.9 respectively.

The Smith Waterman algorithm for sequence alignment is one of the main tools of bioinformatics. It is used for sequence similarity searches and alignment of similar sequences. The high end Graphical Processing Unit (GPU), used for processing graphics on desktop computers, deliver computational capabilities exceeding those of CPUs by an order of magnitude. Recently these capabilities became accessible for general purpose computations thanks to CUDA programming environment on Nvidia GPUs and ATI Stream Computing environment on ATI GPUs. Here we present an efficient implementation of the Smith Waterman algorithm on the Nvidia GPU. The algorithm achieves more than 3.5 times higher per core performance than previously published implementation of the Smith Waterman algorithm on GPU, reaching more than 70% of theoretical hardware performance. The differences between current and earlier approaches are described showing the example for writing efficient code on GPU.

In positron emission tomography (PET), 3D iterative image reconstruction methods have a huge computational burden. In this paper, we developed a list-mode image reconstruction method using graphics processing units (GPUs). Efficiency of acceleration for GPU implementation largely depends on the method chosen, where a reduced number of conditional statements and a reduced memory size are required. On the other hand, accurate system models are required to improve the quality of reconstructed images. Various accurate system models for conventional CPU implementation have been proposed, but these models basically require many conditional statements and huge memory size. Therefore, we developed a new system model which matches GPU implementation better. In this model, the detector response functions, which vary depending on each line of response (LOR), are pre-computed in CPUs and modeled by sixth-order polynomial functions in order to reduce the memory size occupied in GPUs. Each element of a system matrix is obtained on-the-fly in GPUs by calculating the distance between an LOR and a voxel. Therefore the developed system model enables efficient GPU implementation of the accurate system modeling with a reduced number of conditional statements and a reduced memory size. We applied the developed method to a small OpenPET prototype, in which 4-layered depth-of-interaction (DOI) detectors were used. For image reconstruction, we used the dynamic row-action maximum likelihood algorithm (DRAMA). Compared with a conventional model for GPU implementation, in which DRFs are given as a Gaussian function of fixed width, we saw no remarkable difference for DOI data, but for non-DOI data the proposed model outperformed the conventional at the peripheral region of the field-of-view. The proposed model had almost the same calculation time as the conventional model did. For further acceleration, we tried parallel GPU implementation, and we obtained 3.8-fold acceleration by using 4 GPUs.