CPUGPU Collaborated Computation Models for Biological Sequence Alignment with MirrorBased Work Load Balancing Biological sequence alignment has been used in many application areas of computational biology and bioinformatics and both nonheuristic and heuristic algorithms have been developed and used. Although the high accuracy is guaranteed, nonheuristic approaches, such as SmithWaterman algorithm, are not popularly used in the real world due to the quadratic time complexity. However, recent technological development on HPC systems made researchers propose a diversified acceleration approaches of the SmithWaterman algorithm, including GPU based approaches. In this paper, we propose efficient CPUGPU collaborated computation models for protein sequence alignment based on the SmithWaterman algorithm to exploit the maximum efficiency on the heterogeneous system with multicore processor(s) and GPU(s). The proposed approach implements the full functionalities of the local sequence alignment, which are BLAST compatible, and uses efficient strategies for work load balancing and exploiting the maximum performance on the heterogeneous system. Our experimental results showed that the best CPUGPU collaborated computation model outperforms the corresponding serial and the basic GPU computation models with 30.5x and 2.78x speedups, respectively, on the system with two Xeon E52670 processors and a Tesla M2075 GPU.

An Effective Protein Multiple Structure Alignment Using Parallel Computing Multiple spatial alignments of protein structures are an important tool of structural biology. Analysis of protein structures allows us to establish their homology; i.e., the origin from a common ancestor. The rapid growth in the number of known protein structures determines the requirements for the speed of the spatial alignment algorithms. This paper proposes a strategy for using parallel computations to efficiently construct multiple spatial equalizations using multicore cluster system. The developed algorithm is based on the wellproven sequential method of spatial alignment of Multiple Alignments with Translations and Twists (MATT). Results show that the best speedup (38.44) and the least difference between the experimental and theoretical efficiency (0.01) was obtained. The speedup and efficiency based on (128) nodes have been evaluated using LinkSCEEM2 systems at Bibliotheca Alexandrina, Egypt.