CUDA ClustalW: An efficient parallel algorithm for progressive multiple sequence alignment on Multi-GPUs

For biological applications, sequence alignment is an important strategy to analyze DNA and protein sequences. Multiple sequence alignment is an essential methodology to study biological data, such as homology modeling, phylogenetic reconstruction and etc. However, multiple sequence alignment is a NP-hard problem. In the past decades, progressive approach has been proposed to successfully align multiple sequences by adopting iterative pairwise alignments. Due to rapid growth of the next generation sequencing technologies, a large number of sequences can be produced in a short period of time. When the problem instance is large, progressive alignment will be time consuming. Parallel computing is a suitable solution for such applications, and GPU is one of the important architectures for contemporary parallel computing researches. Therefore, we proposed a GPU version of ClustalW v2.0.11, called CUDA ClustalW v1.0, in this work. From the experiment results, it can be seen that the CUDA ClustalW v1.0 can achieve more than 33× speedups for overall execution time by comparing to ClustalW v2.0.11.     

CUDA parallel programming for simulation of epidemiological models based on individuals

Mathematical models are of great value in epidemiology to help understand the dynamics of the various infectious diseases, as well as in the conception of effective control strategies. The classical approach is to use differential equations to describe, in a quantitative manner, the spread of diseases within a particular population. An alternative approach is to represent each individual in the population as a string or vector of characteristic data and simulate the contagion and recovery processes by computational means. This type of model, referred in the literature as MBI (models based on individuals), has the advantage of being flexible as the characteristics of each individual can be quite complex, involving, for instance, age, sex, pre‐existing health conditions, environmental factors, social habits, etc. However, when it comes to simulations involving large populations, MBI may require a large computational effort in terms of memory storage and processing time. In order to cope with the problem of heavy computational effort, this paper proposes a parallel implementation of MBI using a graphics processor unit compatible with CUDA. It was found that, even in the case of a simple susceptible–infected–recovered model, the computational gains in terms of processing time are significant. Copyright © 2015 John Wiley & Sons, Ltd.     

Evaluation of the deflated preconditioned CG method to solve bubbly and porous media flow problems on GPU and CPU

In both bubbly and porous media flow, the jumps in coefficients may yield an ill‐conditioned linear system. The solution of this system using an iterative technique like the conjugate gradient (CG) is delayed because of the presence of small eigenvalues in the spectrum of the coefficient matrix. To accelerate the convergence, we use two levels of preconditioning. For the first level, we choose between out‐of‐the‐box incomplete LU decomposition, sparse approximate inverse, and truncated Neumann series‐based preconditioner. For the second level, we use deflation. Through our experiments, we show that it is possible to achieve a computationally fast solver on a graphics processing unit. The preconditioners discussed in this work exhibit fine‐grained parallelism. We show that the graphics processing unit version of the two‐level preconditioned CG can be up to two times faster than a dual quad core CPU implementation. John Wiley & Sons, Ltd.     

Implementation of the moving particle semi-implicit method on GPU

The Moving Particle Semi-implicit (MPS) method performs well in simulating violent free surface flow and hence becomes popular in the area of fluid flow simulation. However, the implementations of searching neighbouring particles and solving the large sparse matrix equations (Poisson-type equation) are very time-consuming. In order to utilize the tremendous power of parallel computation of Graphics Processing Units (GPU), this study has developed a GPU-based MPS model employing the Compute Unified Device Ar...     

Current performance gains from utilizing the GPU or the ASIC MDGRAPE‐3 within an enhanced Poisson Boltzmann approach

Scientific applications do frequently suffer from limited compute performance. In this article, we investigate the suitability of specialized computer chips to overcome this limitation. An enhanced Poisson Boltzmann program is ported to the graphics processing unit and the application specific integrated circuit MDGRAPE‐3 and resulting execution times are compared to the conventional performance obtained on a modern central processing unit. Speed Up factors are measured and an analysis of numerical accuracy is provided. On both specialized architectures the improvement is increasing with problem size and reaches up to a Speed Up factor of 39 × for the largest problem studied. This type of alternative high performance computing can significantly improve the performance of demanding scientific applications. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009     

基于GPU计算的并行PLS算法研究与实现

偏最小二乘算法(PLS)是与红外、近红外光谱分析结合使用最为广泛的化学计量学算法,然而当前PLS算法通常采用单线程方式实现,当校正模型数量多或样本数量大、波长点数和主成分数较多,模型需对光谱预处理和波长选择方法反复优化时,计算十分缓慢。为大幅提高建模速度,该文提出了一种基于图形处理器(GPU)的并行计算策略,利用具有大规模并行计算特性的GPU作为计算设备,结合CUBLAS库函数实现了基于GPU并行的PLS建模算法(CUPLS)。利用近红外光谱数据集进行性能对比实验,结果表明CUPLS建模算法较传统单线程实现的PLS算法,加速比可达近42倍,极大地提升了化学计量学算法的建模效率。该方法亦可用于其它化学计量学算法的加速。     

GPU-based high-precision real-time radiometric rendering for IR scene generation

Aiming at the problem that traditional infrared scene real-time radiometric rendering method leads to greater calculation error for securing real-time purpose, this article studies the IR rendering comprehensive optimization method, which secures real-time performance as well as calculation accuracy. Firstly, based on the effective average value principle, the spectrum coupling thermal emission and reflected radiations in the spectral radiometric equation are decomposed into physical quantities, and the spectral radiometric equation is improved to become a simpler calculation between “primer” radiance terms and effective average factors. Secondly, the parameter processing method is proposed to cope with the situation when index parameters of effective average factors exceed the maximum dimensionalities of graphics processing unit (GPU) look-up-table (LUT); and pre-calculation method is applied to promote the real-time evaluation efficiency of the physical quantities in the radiometric equation. Finally, concurrent computation of radiometric equation is achieved with GPU IR scene generation software and the precise and real-time rendering of three-dimensional IR scene is realized.     

GALAMOST: GPU‐accelerated large‐scale molecular simulation toolkit

GALAMOST [graphics processing unit (GPU)‐accelerated large‐scale molecular simulation toolkit] is a molecular simulation package designed to utilize the computational power of GPUs. Besides the common features of molecular dynamics (MD) packages, it is developed specially for the studies of self‐assembly, phase transition, and other properties of polymeric systems at mesoscopic scale by using some lately developed simulation techniques. To accelerate the simulations, GALAMOST contains a hybrid particle‐field MD technique where particle–particle interactions are replaced by interactions of particles with density fields. Moreover, the numerical potential obtained by bottom‐up coarse‐graining methods can be implemented in simulations with GALAMOST. By combining these force fields and particle‐density coupling method in GALAMOST, the simulations for polymers can be performed with very large system sizes over long simulation time. In addition, GALAMOST encompasses two specific models, that is, a soft anisotropic particle model and a chain‐growth polymerization model, by which the hierarchical self‐assembly of soft anisotropic particles and the problems related to polymerization can be studied, respectively. The optimized algorithms implemented on the GPU, package characteristics, and benchmarks of GALAMOST are reported in detail. © 2013 Wiley Periodicals, Inc.