Computer Science ›› 2017, Vol. 44 ›› Issue (4): 182-187.doi: 10.11896/j.issn.1002-137X.2017.04.040
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YIN Meng-jia, XU Xian-bin, HE Shui-bing, HU Jing, YE Cong-huan and ZHANG Tao
| [1] SHERESHEVSKY M,CUKIC B,CROWEL J,et al.Software Aging and Multifractality of Memory Resources[C]∥Procee-dings of DSN 2003.Los Alamitos,USA:IEEE Computer Society,2003:721-730. [2] BAI H T,OUYANG D T,LI X M,et al.Optimizing Sparse Matrix-vecotr Multiplication Based on GPU[J].Computer Science,2010,7(8):168-171,1.(in Chinese) 白洪涛,欧阳丹彤,李熙铭,等.基于GPU的稀疏矩阵向量乘优化[J].计算机科学,2010,7(8):168-171,1. [3] BELL N,GARLAND M.Implementing Sparse Matrix-VectorMultiplication On Throughput-Oriented Processors[C]∥Proceedings of the Conference on High Performance Computing Networking.New York:Association for Computing Machinery,2009:1-11. [4] RESIOS A.GPU Performance Prediction Using ParametrizedModels[D].Utrecht:Utrecht University,2011. [5] VZQUEZ F,GARZON E M,FEMANDEZ J J.A matrix approach to grahpic reconstruction and its implementation on GPUs[J].Journal of Structural Biology,2010,0(1):146-151. [6] BIK A J C,WIJSHOFF H A G.Automatic data structure selection and transformation for sparse matrix computations[J].IEEE Transactions on Parallel and Distributed Systems,1996,7(2):109-126. [7] IM E J,YELICK K.Optimizing Sparse Matrix-Vector Multiplication on SMPs[EB/OL].http://www.cs.berkeley.edu/yelick/ejim/ppsc99.ps. [8] LEE BENJIAMIN C,VUDUC RICHARD W,YELICK D J W,et al.Performance Model for Evaluation and Automatic Tuning of Symmetric Sparse Matrix-Vector Multiply[C]∥2004 International Conference on Parallel Processing.United States:IEEE Computer Society,2004:169-174. [9] FATAHAIAN K,SUGERMAN J,HANRAHAN P.Under-standing the efficiency of GPU algorithms for matrix-matrix multiplications[C]∥Proceedings of the SIGGRAPH/Eurographics Workshop on Graphics Hardware.New York:Association for Computing Machinery,2004:133-138. [10] BOLZ J,FARMER I,GRINSPUN E,et al.Sparse Matrix Sol-vers on the GPU:Conjugate Gradients and Multigrid[J].ACM Transactions on Graphics,2003,2(3):917-924. [11] VZQUEZ F,ORTEGA G,FERNANDEZ J J,et al.Improving the Performance of the Sparse Matrix Vector Product with GPUs[C]∥10th IEEE International Conference on Computer and Information Technology.Bradford,United States:IEEE Computer Society,2010:1146-1151. [12] KORNILIOS K,GEORGIOS G,NECTARIOS K.Improving the performance of multithreaded sparse matrix-vector multiplication using index and value compression[C]∥Proceedings of the International Conference on Parallel Processing.United States:Institute of Electrical and Electronics Engineers,2008:511-519. [13] MONAKOV A,AVETISYAN A.Implementing Blocked Sparse Matrix Vector Multiplication on NVIDIA GPUs[C]∥9th International Workshop on Embedded Computer Systems:Architectures,Modeling,and Simulation.Germany:Springer Verlag,2009,5657:289-297. [14] BULUC A,FINEMAN J T,MATTEO F,et al.Parallel Sparse Matrix-Vector and Matrix-Transpose-Vector Multiplication Using Compressed Sparse Blocks[C]∥Proceedings of the Twenty-first Annual Symposium on Parallelism in Algorithms and Architectures.New York:Association for Computing Machinery,2009:233-244. [15] XU S M,XUE W,LIN H X.Performance Modeling and Optimization of Sparse Matrix-Vector Multiplication on NVIDIA CUDA Platform[J].Journal of Supercomputing,2013,3(3):710-721. [16] ZHANG Y,OWENS J D.A Quantitative Performance Analysis Model for GPU Architectures[C]∥International Symposium on High-Performance Computer Architecture.United states:IEEE Computer Society,2011:382-393. [17] SARA S B,MATTHIEU D,PATEL S J,et al.An AdaptivePerformance Modeling Tool for GPU Architectures[C]∥Proceedings of the 2010 ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming.New York:Association for Computing Machinery,2010:105-114. [18] HONG S,KIM H.An Analytical Model for a GPU Architecture with Memory-Level and Thread-Level Parallelism Awareness[C]∥International Symposium on Computer Architecture.United States:Institute of Electrical and Electronics Engineers,2009:152-163. [19] KISHORE K,RISHABH M,REHMAN S M,et al.A Performance Prediction Model for the CUDA GPGPU Platform[C]∥16th International Conference on High Performance Computing.United States:IEEE Computer Society,2009:463-472. [20] LIANG T.The Research on Optimizing Sparse Matrix Computation Based on GPU[D].Wuhan:Huazhong University of Scien-ce and Technology,2012.(in Chinese) 梁添.基于GPU的稀疏矩阵运算优化研究[D].武汉:华中科技大学,2012. [21] BARRETT R,et al.Templates for the solution of Linear Systems:Building blocks for Iterative Methods[R].Philadelphia:SIAM Press,1994. [22] RUKHSANA S,ANILA U,CHUGTAI I R.Implementationand Evaluation of Parallel Sparse Matrix-Vector Products on Distributed Memory Parallel Computers[C]∥IEEE International Conference on Cluster Computing.United States:Institute of Electrical and Electronics Engineers,2006. [23] WANG Z W,CHENG L L,ZHAO W Q.Parallel Computer Performance Analysis Model Based on GPU[J].Computer Science,2014,41(1):31-38.(in Chinese) 王卓薇,程良伦,赵武清.基于GPU的并行计算性能分析模型[J].计算机科学,2014,41(1):31-38. [24] PING G,WANG L Q,CHEN P.A Performance Modeling and Optimization Analysis Tool for Sparse Matrix-Vector Multiplication on GPUs[J].IEEE Transactions on Parallel And Distributed Systems,2014,5(5):1112-1123. [25] DAVIS TIMOTHY A,HU Y F.The University of Florida Sparse Matrix Collection[J].ACM Transactions on Mathematical Software,2011,8(1):1-28. [26] SAMUEL W,LEONID O,RICHARD V,et al.Optimization of Sparse Matrix-Vector Multiplication on Emerging Multicore Platforms[J].Parallel Computing,2009,5(3):178-194. [27] VASILEIOS K,GOUMAS G,NECTARIOS K.Performance Mo-dels for Blocked Sparse Matrix-vector Multiplication Kernels[C]∥2009 International Conference on Parallel Processing.United States:IEEE,2009:356-364. [28] FENG X W.Research on Key Technology of Similarity Calculation based on GPU[D].Wuhan:Huazhong University of Science and Technology,2014.(in Chinese) 冯晓文.基于GPU的相似度计算关键技术研究[D].武汉:华中科技大学,2014. [29] CHEN R X.Parallelized XML Query Based on Functional Intermediate Language[J].Journal of Chongqing Uiversity of Technology(Natural Science),2011,5(7):81-86.(in Chinese) 陈荣鑫.基于函数式中间语言的XML查询并行化[J].重庆理工大学学报(自然科学),2011,5(7):81-86. |
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