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10.1155/2015/185179 http://scihub22266oqcxt.onion/10.1155/2015/185179 C4538332!4538332!26339591     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Res+Int 2015 ; 2015 (ä): ä Nephropedia Template TP {{tp|p=26339591|t=2015. Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs |pdf=|usr=}}{{26339591}} gab.com Text Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26339591 #FOAvip Sequence alignment lies at heart of the bioinformatics. The Smith-Waterman algorithm is one of the key sequence search algorithms and has gained popularity due to improved implementations and rapidly increasing compute power. Recently, the Smith-Waterman algorithm has been successfully mapped onto the emerging general-purpose graphics processing units (GPUs). In this paper, we focused on how to improve the mapping, especially for short query sequences, by better usage of shared memory. We performed and evaluated the proposed method on two different platforms (Tesla C1060 and Tesla K20) and compared it with two classic methods in CUDASW++. Further, the performance on different numbers of threads and blocks has been analyzed. The results showed that the proposed method significantly improves Smith-Waterman algorithm on CUDA-enabled GPUs in proper allocation of block and thread numbers. Twit Text FOAVip Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26339591 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26339591 #FOAvip English Wikipedia <ref>{{Cite pmid|26339591}}</ref> Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs #MMPMID26339591 Huang LT; Wu CC; Lai LF; Li YJ Biomed Res Int 2015[]; 2015 (ä): ä PMID26339591show ga Sequence alignment lies at heart of the bioinformatics. The Smith-Waterman algorithm is one of the key sequence search algorithms and has gained popularity due to improved implementations and rapidly increasing compute power. Recently, the Smith-Waterman algorithm has been successfully mapped onto the emerging general-purpose graphics processing units (GPUs). In this paper, we focused on how to improve the mapping, especially for short query sequences, by better usage of shared memory. We performed and evaluated the proposed method on two different platforms (Tesla C1060 and Tesla K20) and compared it with two classic methods in CUDASW++. Further, the performance on different numbers of threads and blocks has been analyzed. The results showed that the proposed method significantly improves Smith-Waterman algorithm on CUDA-enabled GPUs in proper allocation of block and thread numbers. äImproving the Mapping of Smith-Waterman Sequence Database Searches ont(epmc).pdf DeepDyve Pubget Overpricing