Accelerating Smith-Waterman Algorithm for Faster Sequence Alignment using Graphical Processing Unit

In bioinformatics, sequence alignment is very important task to compare and find similarity between biological sequences. Smith Waterman algorithm is most widely used for alignment process but it has quadratic time complexity. This algorithm is using sequential approach so if the no. of biological sequences is increasing then it takes too much time to align sequences. In this paper, parallel approach of Smith Waterman algorithm is proposed and implemented according to the architecture of graphic processing unit using CUDA in which features of GPU is combined with CPU in such a way that alignment process is three times faster than sequential implementation of Smith Waterman algorithm and helps in accelerating the performance of sequence alignment using GPU. This paper describes the parallel implementation of sequence alignment using GPU and this intra-task parallelization strategy reduces the execution time. The results show significant runtime savings on GPU.

CPU AND GPU PERFORMANCE ANALYSIS ON 2D MATRIX OPERATION

GPU or Graphic Processing Unit can be used on many platforms in general GPUs are used for rendering graphics but now GPUs are general purpose parallel processors with support for easily accessible programming interfaces and industry standard languages such as C, Python and Fortran. In this study, the authors will compare CPU and GPU for completing some matrix calculation. To compare between CPU and GPU, the authors have done some testing to observe the use of Processing Unit, memory and computing time to complete matrix calculations by changing matrix sizes and dimensions. The results of tests that have been done shows asynchronous GPU is faster than sequential. Furthermore, thread for GPU needs to be adjusted to achieve efficiency in GPU load.

FUNGSIONALITAS PEMROGRAMAN BERBASIS CPU-GPU DALAM SIMULASI PENJALARAN TSUNAMI

Indonesia merupakan daerah rawan gempa yang memicu timbulnya tsunami. Salah satu cara menganalisis gelombang tsunami adalah dengan membangun sistem simulasi penjalarannya. Penjalaran gelombang tsunami dapat dimodelkan secara matematis dengan Shallow Water Equation (SWE) sebagai gelombang non-dispersive, yang berarti bahwa gelombang dengan panjang gelombang berbeda menjalar dengan kecepatan yang sama. Pengembangan GPU (Graphic Processing Unit) dalam aplikasi numerik menjadi salah satu solusi dalam penyelesaian persamaan penjalaran tsunami tersebut. Library CUDA dalam GPU dapat digunakan sebagai komputasi parallel sehingga mempercepat proses perhitungan. Dalam penelitian ini berhasil dibangun program untuk memodelkan penjalaran gelombang tsunami dengan bahasa pemrograman C berbasis CPU-GPU, dengan penyelesaian yang fungsional dan 9 kali lebih cepat dari pemrograman berbasis CPU.