Accelerating turbo similarity searching in chemoinformatics on multicore and GPU platforms
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POSTER PRESENTATION Open Access Accelerating turbo similarity searching in chemoinformatics on multicore and GPU platforms Marwah Haitham Al Laila * , Nurul Malim, Nur’Aini Abdul Rashid From 9th German Conference on Chemoinformatics Fulda, Germany. 10-12 November 2013 The increase in the database size of chemical compound requires a longer time in processing for any searching algorithms [1]. With the focus on an algorithm called Turbo Similarity Searching which have been proven to have good accuracy in retrieving actives [2], we propose that this algorithm be implemented on the widely-used many-cores and multi-cores processors which are easily available at lower cost. This would help medicinal che- mist runs virtual screening faster while maintaining the accuracy. The many-cores processors are on-chip processors that could run simultaneously at a clock-cycle. Whilst the multi-cores processors are originally being devel- oped to support graphics processing hence being called Graphics Processing Unit (GPU). However, the usage of GPU is now being generalized to include other general purpose operation [3]. Many works on the parallel field have tried implementing computational algorithms on this unit [4]. Taken into consideration on the compute intensive characteristics of TSS, we investigate the best method to parallelize it for better execution time. TSS is a two- phase algorithm in which its second phase is a compute intensive portion of the algorithm. Hence, if this phase could be accelerated, TSS would run faster and search- ing through a larger database would not be less a pro- blem. This poster describes our experimental details and results on the matter. Published: 11 March 2014 References 1. Pu Liu DKA, Yang E: Accelerating Chemical Database Searching Using Graphics Processing Units. J Chem. Inf. Model 2011, 51:1807-1816. 2. Malim N: Enhancing Similarity Searching. PhD Thesis University of Sheffield, UK; 2011. 3. Harish P, Narayanan P: Accelerating large graph algorithms on the GPU using CUDA. High Performance Computing–HiPC 2007, 197-208. 4. Maggioni M, Santambrogio MD, Liang J: GPU-accelerated Chemical Similarity Assessment for Large Scale Databases. Procedia Computer Science 2007, 4:2007-2016. doi:10.1186/1758-2946-6-S1-P38 Cite this article as: Al Laila et al.: Accelerating turbo similarity searching in chemoinformatics on multicore and GPU platforms. Journal of Cheminformatics 2014 6(Suppl 1):P38. Open access provides opportunities to our colleagues in other parts of the globe, by allowing anyone to view the content free of charge. Publish with ChemistryCentral and every scientist can read your work free of charge W. Jeffery Hurst, The Hershey Company. available free of charge to the entire scientific community peer reviewed and published immediately upon acceptance cited in PubMed and archived on PubMed Central yours you keep the copyright Submit your manuscript here: http://www.chemistrycentral.com/manuscript/ * Correspondence: [email protected] School of Computer Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia Al Laila et al. Journal of Cheminformatics 2014, 6(Suppl 1):P38 http://www.jcheminf.com/content/6/S1/P38 © 2014 Al Laila et al; licensee Chemistry Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Transcript of Accelerating turbo similarity searching in chemoinformatics on multicore and GPU platforms
POSTER PRESENTATION Open Access
Accelerating turbo similarity searching inchemoinformatics on multicore and GPUplatformsMarwah Haitham Al Laila*, Nurul Malim, Nur’Aini Abdul Rashid
From 9th German Conference on ChemoinformaticsFulda, Germany. 10-12 November 2013
The increase in the database size of chemical compoundrequires a longer time in processing for any searchingalgorithms [1]. With the focus on an algorithm calledTurbo Similarity Searching which have been proven tohave good accuracy in retrieving actives [2], we proposethat this algorithm be implemented on the widely-usedmany-cores and multi-cores processors which are easilyavailable at lower cost. This would help medicinal che-mist runs virtual screening faster while maintaining theaccuracy.The many-cores processors are on-chip processors
that could run simultaneously at a clock-cycle. Whilstthe multi-cores processors are originally being devel-oped to support graphics processing hence being calledGraphics Processing Unit (GPU). However, the usage ofGPU is now being generalized to include other generalpurpose operation [3]. Many works on the parallel fieldhave tried implementing computational algorithms onthis unit [4].Taken into consideration on the compute intensive
characteristics of TSS, we investigate the best method toparallelize it for better execution time. TSS is a two-phase algorithm in which its second phase is a computeintensive portion of the algorithm. Hence, if this phasecould be accelerated, TSS would run faster and search-ing through a larger database would not be less a pro-blem. This poster describes our experimental details andresults on the matter.
Published: 11 March 2014
References1. Pu Liu DKA, Yang E: Accelerating Chemical Database Searching Using
Graphics Processing Units. J Chem. Inf. Model 2011, 51:1807-1816.2. Malim N: Enhancing Similarity Searching. PhD Thesis University of
Sheffield, UK; 2011.3. Harish P, Narayanan P: Accelerating large graph algorithms on the GPU
using CUDA. High Performance Computing–HiPC 2007, 197-208.4. Maggioni M, Santambrogio MD, Liang J: GPU-accelerated Chemical
Similarity Assessment for Large Scale Databases. Procedia ComputerScience 2007, 4:2007-2016.
doi:10.1186/1758-2946-6-S1-P38Cite this article as: Al Laila et al.: Accelerating turbo similarity searchingin chemoinformatics on multicore and GPU platforms. Journal ofCheminformatics 2014 6(Suppl 1):P38.
Open access provides opportunities to our colleagues in other parts of the globe, by allowing
anyone to view the content free of charge.
Publish with ChemistryCentral and everyscientist can read your work free of charge
W. Jeffery Hurst, The Hershey Company.
available free of charge to the entire scientific communitypeer reviewed and published immediately upon acceptancecited in PubMed and archived on PubMed Centralyours you keep the copyright
Submit your manuscript here:http://www.chemistrycentral.com/manuscript/
* Correspondence: [email protected] of Computer Sciences, Universiti Sains Malaysia, 11800, Penang,Malaysia
Al Laila et al. Journal of Cheminformatics 2014, 6(Suppl 1):P38http://www.jcheminf.com/content/6/S1/P38
© 2014 Al Laila et al; licensee Chemistry Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
