1,721,002 research outputs found
Multiple alignment through protein secondary-structure information
No full textIt is well known that protein secondary structure information can help the process of performing multiple align- ment, in particular when the amount of similarity among the involved sequences moves towards the “twilight zone” (less than 30% of pairwise similarity). In this paper, a multiple alignment algorithm is presented, explicitly designed for exploiting any available secondary structure information. A layered architecture with two interacting levels has been defined for dealing with both primary and secondary structure information of target sequences. Secondary structure (either available or predicted by resorting to a technique based on multiple experts) is used to calculate an initial alignment at the secondary level, to be arranged by locally- scoped operators devised to refine the alignment at the primary level. Aimed at evaluating the impact of secondary information on the quality of alignments, in particular alignments with a low degree of similarity, the technique has been implemented and assessed on relevant test cases
CUDA-quicksort: An improved GPU-based implementation of quicksort
No full textSorting is a very important task in computer science and becomes a critical operation for programs that
make heavy use of sorting algorithms, in particular when dealing with huge amounts of data. Generalpurpose
computing has been successfully used on Graphics Processing Units (GPUs) to parallelize some
sorting algorithms. Two GPU-based implementations of the quicksort algorithm were presented in literature:
the GPU-quicksort, a CUDA iterative implementation, and the NVIDIA CUDA Dynamic Parallel (CDP)
advanced quicksort, a recursive implementation. In this article we propose CUDA-Quicksort a new blockoriented
iterative GPU-based implementation of the sorting algorithm. CUDA-Quicksort has been designed
starting from GPU-Quicksort. Unlike GPU-Quicksort, it uses atomic primitives to perform inter-block
communications while ensuring an optimized access to the GPU memory.
Experiments performed on six sorting benchmark distributions show that CUDA-Quicksort is up to four
times faster than the iterative GPU-Quicksort and up to three times faster than the recursive NVIDIA CDPQuicksort.
An in depth analysis of the performance between the proposed CUDA-Quicksort and the GPUQuicksort
show that the main improvement is related to the optimized GPU memory access rather than to the
use of the atomic primitives. Moreover, with the aim to assess the advantages of using the CUDA dynamic
parallelism, we also implemented a recursive version of the CUDA-Quicksort. Experimental results show
that the proposed implementation is faster than the one provided by NVIDIA, with better performance
achieved using the iterative implementation
Docking and Molecular dynamics protocol for ligand binding characterization of Nuclear Receptors
No full text
Hotspot residues involved in potency and selectivity of the ligand binding in Nuclear Receptors.
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