{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T07:47:14Z","timestamp":1759132034686},"reference-count":35,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2013,8,23]],"date-time":"2013-08-23T00:00:00Z","timestamp":1377216000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2014,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>The integration of the latest breakthroughs in computational modeling and high performance computing (HPC) has leveraged advances in the fields of healthcare and drug discovery, among others. By integrating all these developments together, scientists are creating new exciting personal therapeutic strategies for living longer that were unimaginable not that long ago. However, we are witnessing the biggest revolution in HPC in the last decade. Several graphics processing unit architectures have established their niche in the HPC arena but at the expense of an excessive power and heat. A solution for this important problem is based on heterogeneity. In this paper, we analyze power consumption on heterogeneous systems, benchmarking a bioinformatics kernel within the framework of virtual screening methods. Cores and frequencies are tuned to further improve the performance or energy efficiency on those architectures. Our experimental results show that targeted low\u2010cost systems are the lowest power consumption platforms, although the most energy efficient platform and the best suited for performance improvement is the Kepler GK110 graphics processing unit from Nvidia by using compute unified device architecture. Finally, the open computing language version of virtual screening shows a remarkable performance penalty compared with its compute unified device architecture counterpart. Copyright \u00a9 2013 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/cpe.3119","type":"journal-article","created":{"date-parts":[[2013,8,23]],"date-time":"2013-08-23T14:38:46Z","timestamp":1377268726000},"page":"1832-1846","source":"Crossref","is-referenced-by-count":8,"title":["Toward energy efficiency in heterogeneous processors: findings on virtual screening methods"],"prefix":"10.1002","volume":"26","author":[{"given":"Gin\u00e9s D.","family":"Guerrero","sequence":"first","affiliation":[{"name":"Department of Computer Architecture University of Murcia  30080 Murcia Spain"}]},{"given":"Juan M.","family":"Cebri\u00e1n","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science  7034 Trondheim Norway"}]},{"given":"Horacio","family":"P\u00e9rez\u2010S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Department of Computer Science Catholic University of Murcia  30107 Murcia Spain"}]},{"given":"Jos\u00e9 M.","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"Department of Computer Architecture University of Murcia  30080 Murcia Spain"}]},{"given":"Manuel","family":"Ujald\u00f3n","sequence":"additional","affiliation":[{"name":"Department of Computer Architecture University of Malaga  29071 Malaga Spain"}]},{"given":"Jos\u00e9 M.","family":"Cecilia","sequence":"additional","affiliation":[{"name":"Department of Computer Science Catholic University of Murcia  30107 Murcia Spain"}]}],"member":"311","published-online":{"date-parts":[[2013,8,23]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"AsanovicK BodikR CatanzaroBC GebisJJ HusbandsP KeutzerK PattersonDA PlishkerWL ShalfJ WilliamsSW YelickKA.The landscape of parallel computing research: a view from berkeley.Technical report UCB\/EECS\u20102006\u2010183 EECS Department University of California Berkeley December2006."},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2000064.2000108"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1096361"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1039\/C3RA23502E"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci049714+"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci7000346"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1529\/biophysj.106.084301"},{"key":"e_1_2_9_9_1","unstructured":"NVIDIA.NVIDIA CUDA C Programming Guide 5.5. 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