Abstract
The problems arising due to massive data storage and data analysis can be handled by recent technologies, like cloud computing and parallel computing. MapReduce, MPI, CUDA, OpenMP, OpenCL are some of the widely available tools and techniques that use multithreading approach. However, it is a challenging task to use these technologies effectively to handle the compute intensive problems in the fields like life science, environment, fluid dynamics, image processing, etc. In this paper, we have used many core platforms with graphics processing units (GPU) to implement one of very important and fundamental problem of sequence alignment in the field of bioinformatics. Dynamic and concurrent kernel features offered by graphics card are used to speed up the performance. With these features, we achieved a speed up of around 120X and 55X. We have coupled well-known tiling technique with these features and observed a performance improvement up to 4X and 2X, as compared to non-tiling execution. The paper also analyses resource parameters, GPU occupancy and proposes their relationship with the design parameters for the chosen algorithm. These observations have been quantified and the relationship between the parameters is presented. The results of study can be extended further to study similar algorithms in this area.
Highlights
Graphics hardware along with multi-core system has emerged as a new combination for the applications that has computationally demanding tasks to be performed
The main aim of this paper is to study and analyse the huge computational power offered by the graphics processors and utilize it to enhance the performance of a well-known problem of pair-wise sequence alignment
The main focus of our study was to explore the features of the graphics cards and map the resource requirement of the algorithm under consideration with the available resources
Summary
Graphics hardware along with multi-core system has emerged as a new combination for the applications that has computationally demanding tasks to be performed. The main aim of this paper is to study and analyse the huge computational power offered by the graphics processors and utilize it to enhance the performance of a well-known problem of pair-wise sequence alignment. With the availability of highly parallel programming platforms, like many and multi core machines, it has become possible to effectively use them to accelerate the performance of data parallel applications. We have presented a method for generating score matrix for pair wise local sequence alignment problem using tilling technique. This method is coupled with the features like dynamic and concurrent kernel execution supported by the GPU card. The approach can be applied to the algorithms like global sequence alignment and multiple sequence alignment
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