By Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Ana Maria A.C. Rocha, Carmelo M. Torre, David Taniar, Bernady O. Apduhan, Elena Stankova, Shangguang Wang
The five-volume set LNCS 9786-9790 constitutes the refereed proceedingsof the sixteenth foreign convention on Computational technology and ItsApplications, ICCSA 2016, held in Beijing, China, in July 2016.
The 239 revised complete papers and 14 brief papers offered at 33 workshops have been conscientiously reviewed and chosen from 849 submissions. they're geared up in 5 thematical tracks: computational equipment, algorithms and clinical purposes; excessive functionality computing and networks; geometric modeling, portraits and visualization; complex and rising functions; and data structures and technologies.
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Extra resources for Computational Science and Its Applications – ICCSA 2016: 16th International Conference, Beijing, China, July 4-7, 2016, Proceedings, Part II
In Japanese) 14. : Improving performance of sparse matrix-vector multiplication. In: Proceedings of the 1999 ACM/IEEE Conference on Supercomputing. SC 1999. ACM (1999) 15. : Performance evaluation of sparse matrix multiplication kernels on intel Xeon Phi. , Wa´sniewski, J. ) Parallel Processing and Applied Mathematics. LNCS, vol. 8384, pp. 559– 570. Springer, Heidelberg (2014) 16. : Accelerating sparse matrix-vector multiplication on GPUs using bit-representation-optimized schemes. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis.
VECPAR 2014. LNCS, vol. 8969, pp. 43–56. K. C. edu Abstract. Wireless sensor networks consist of sensor devices with limited energy resources and computational capabilities, hence network optimization and algorithmic development in minimizing the total energy or power to maintain the connectivity of the underlying network are crucial for their design and maintenance. We consider a generalized system model of wireless sensor networks whose node set is decomposed into multiple clusters, and show that the decision and the induced minimization problems of the cluster-connectivity of wireless sensor networks appear to be computationally intractable – completeness and hardness, respectively, for the nondeterministic polynomial-time complexity class.
Martorell, X. ) HiPEAC 2010. LNCS, vol. 5952, pp. 111–125. Springer, Heidelberg (2010) 13. : Optimized implementation of segmented scan method for CUDA. IPSJ Tech. Rep. 2010-HPC-126(1), 1–7 (2010). (in Japanese) 14. : Improving performance of sparse matrix-vector multiplication. In: Proceedings of the 1999 ACM/IEEE Conference on Supercomputing. SC 1999. ACM (1999) 15. : Performance evaluation of sparse matrix multiplication kernels on intel Xeon Phi. , Wa´sniewski, J. ) Parallel Processing and Applied Mathematics.