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Parallel Computation of Non-Bonded Interactions in Drug Discovery: Nvidia GPUs vs. Intel Xeon Phi.

J. Fang, A. Varbanescu, B. Imbernon, J. Cecilia, and H. Sánchez. IWBBIO, page 579-588. Copicentro Editorial, (2014)

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Evaluating vector data type usage in OpenCL kernels.J. Fang, A. Varbanescu, X. Liao, and H. Sips. Concurrency and Computation: Practice and Experience, 27 (17): 4586-4602 (2015)NEMO5: Achieving High-end Internode Communication for Performance Projection Beyond Moore's Law.R. Andrawis, J. Bermeo, J. Charles, J. Fang, J. Fonseca, Y. He, G. Klimeck, Z. Jiang, T. Kubis, D. Mejia and 5 other author(s). CoRR, (2015)An application-centric evaluation of OpenCL on multi-core CPUs.J. Shen, J. Fang, H. Sips, and A. Varbanescu. Parallel Computing, 39 (12): 834-850 (2013)Aristotle: A performance impact indicator for the OpenCL kernels using local memory.J. Fang, H. Sips, and A. Varbanescu. Scientific Programming, 22 (3): 239-257 (2014)Realistic Performance Characterization of CFD Applications on Intel Many Integrated Core Architecture.Y. Che, C. Xu, J. Fang, Y. Wang, and Z. Wang. Comput. J., 58 (12): 3279-3294 (2015)Parallelizing a High-Order CFD Software for 3D, Multi-block, Structural Grids on the TianHe-1A Supercomputer.C. Xu, X. Deng, L. Zhang, Y. Jiang, W. Cao, J. Fang, Y. Che, Y. Wang, and W. Liu. ISC, volume 7905 of Lecture Notes in Computer Science, page 26-39. Springer, (2013)A Comprehensive Performance Comparison of CUDA and OpenCL.J. Fang, A. Varbanescu, and H. Sips. ICPP, page 216-225. IEEE Computer Society, (2011)An Auto-tuning Solution to Data Streams Clustering in OpenCL.J. Fang, A. Varbanescu, and H. Sips. CSE, page 587-594. IEEE Computer Society, (2011)High Performance Parallel Graph Coloring on GPGPUs.P. Li, X. Chen, Z. Quan, J. Fang, H. Su, T. Tang, and C. Yang. IPDPS Workshops, page 845-854. IEEE Computer Society, (2016)Evaluating the Performance Impact of Multiple Streams on the MIC-Based Heterogeneous Platform.Z. Li, J. Fang, T. Tang, X. Chen, C. Chen, and C. Yang. IPDPS Workshops, page 1341-1350. IEEE Computer Society, (2016)

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Parallel Computation of Non-Bonded Interactions in Drug Discovery: Nvidia GPUs vs. Intel Xeon Phi.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Parallel Computation of Non-Bonded Interactions in Drug Discovery: Nvidia GPUs vs. Intel Xeon Phi.

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Parallel Computation of Non-Bonded Interactions in Drug Discovery: Nvidia GPUs vs. Intel Xeon Phi.