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Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Sparse Matrix Vector Multiplication"

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Tao, Yuan, Yangdong Deng, Shuai Mu, Zhenzhong Zhang, Mingfa Zhu, Limin Xiao, and Li Ruan. "GPU accelerated sparse matrix-vector multiplication and sparse matrix-transpose vector multiplication." Concurrency and Computation: Practice and Experience 27, no. 14 (October 7, 2014): 3771–89. http://dx.doi.org/10.1002/cpe.3415.

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2

Filippone, Salvatore, Valeria Cardellini, Davide Barbieri, and Alessandro Fanfarillo. "Sparse Matrix-Vector Multiplication on GPGPUs." ACM Transactions on Mathematical Software 43, no. 4 (March 23, 2017): 1–49. http://dx.doi.org/10.1145/3017994.

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ERHEL, JOCELYNE. "SPARSE MATRIX MULTIPLICATION ON VECTOR COMPUTERS." International Journal of High Speed Computing 02, no. 02 (June 1990): 101–16. http://dx.doi.org/10.1142/s012905339000008x.

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4

Haque, Sardar Anisul, Shahadat Hossain, and M. Moreno Maza. "Cache friendly sparse matrix-vector multiplication." ACM Communications in Computer Algebra 44, no. 3/4 (January 28, 2011): 111–12. http://dx.doi.org/10.1145/1940475.1940490.

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5

Bienz, Amanda, William D. Gropp, and Luke N. Olson. "Node aware sparse matrix–vector multiplication." Journal of Parallel and Distributed Computing 130 (August 2019): 166–78. http://dx.doi.org/10.1016/j.jpdc.2019.03.016.

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Heath, L. S., C. J. Ribbens, and S. V. Pemmaraju. "Processor-efficient sparse matrix-vector multiplication." Computers & Mathematics with Applications 48, no. 3-4 (August 2004): 589–608. http://dx.doi.org/10.1016/j.camwa.2003.06.009.

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Yang, Xintian, Srinivasan Parthasarathy, and P. Sadayappan. "Fast sparse matrix-vector multiplication on GPUs." Proceedings of the VLDB Endowment 4, no. 4 (January 2011): 231–42. http://dx.doi.org/10.14778/1938545.1938548.

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Romero, L. F., and E. L. Zapata. "Data distributions for sparse matrix vector multiplication." Parallel Computing 21, no. 4 (April 1995): 583–605. http://dx.doi.org/10.1016/0167-8191(94)00087-q.

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Zardoshti, Pantea, Farshad Khunjush, and Hamid Sarbazi-Azad. "Adaptive sparse matrix representation for efficient matrix–vector multiplication." Journal of Supercomputing 72, no. 9 (November 28, 2015): 3366–86. http://dx.doi.org/10.1007/s11227-015-1571-0.

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10

Yzelman, A. N., and Rob H. Bisseling. "Cache-Oblivious Sparse Matrix–Vector Multiplication by Using Sparse Matrix Partitioning Methods." SIAM Journal on Scientific Computing 31, no. 4 (January 2009): 3128–54. http://dx.doi.org/10.1137/080733243.

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Дисертації з теми "Sparse Matrix Vector Multiplication"

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Ashari, Arash. "Sparse Matrix-Vector Multiplication on GPU." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1417770100.

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Ramachandran, Shridhar. "Incremental PageRank acceleration using Sparse Matrix-Sparse Vector Multiplication." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1462894358.

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Balasubramanian, Deepan Karthik. "Efficient Sparse Matrix Vector Multiplication for Structured Grid Representation." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1339730490.

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Mansour, Ahmad [Verfasser]. "Sparse Matrix-Vector Multiplication Based on Network-on-Chip / Ahmad Mansour." München : Verlag Dr. Hut, 2015. http://d-nb.info/1075409470/34.

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Singh, Kunal. "High-Performance Sparse Matrix-Multi Vector Multiplication on Multi-Core Architecture." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524089757826551.

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El-Kurdi, Yousef M. "Sparse Matrix-Vector floating-point multiplication with FPGAs for finite element electromagnetics." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98958.

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Анотація:
The Finite Element Method (FEM) is a computationally intensive scientific and engineering analysis tool that has diverse applications ranging from structural engineering to electromagnetic simulation. Field Programmable Gate Arrays (FPGAs) have been shown to have higher peak floating-point performance than general purpose CPUs, and the trends are moving in favor of FPGAs. We present an architecture and implementation of an FPGA-based Sparse Matrix-Vector Multiplier (SMVM) for use in the iterative solution of large, sparse systems of equations arising from FEM applications. Our architecture exploits the FEM matrix sparsity structure to achieve a balance between performance and hardware resource requirements. The architecture is based on a pipelined linear array of Processing Elements (PEs). A hardware-oriented matrix "striping" scheme is developed which reduces the number of required processing elements. The implemented SMVM-pipeline prototype contains 8 PEs and is clocked at 110 MHz obtaining a peak performance of 1.76 GFLOPS. For 8 GB/s of memory bandwidth typical of recent FPGA reconfigurable systems, this architecture can achieve 1.5 GFLOPS sustained performance. A single pipeline uses 30% of the logic resources and 40% of the memory resources of a Stratix S80 FPGA. Using multiple instances of the pipeline, linear scaling of the peak and sustained performance can be achieved. Our stream-through architecture provides the added advantage of enabling an iterative implementation of the SMVM computation required by iterative solvers such as the conjugate gradient method, avoiding initialization time due to data loading and setup inside the FPGA internal memory.
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Godwin, Jeswin Samuel. "High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1357280824.

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Kunchum, Rakshith. "On Improving Sparse Matrix-Matrix Multiplication on GPUs." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492694387445938.

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Pantawongdecha, Payut. "Autotuning divide-and-conquer matrix-vector multiplication." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105968.

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Анотація:
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 73-75).
Divide and conquer is an important concept in computer science. It is used ubiquitously to simplify and speed up programs. However, it needs to be optimized, with respect to parameter settings for example, in order to achieve the best performance. The problem boils down to searching for the best implementation choice on a given set of requirements, such as which machine the program is running on. The goal of this thesis is to apply and evaluate the Ztune approach [14] on serial divide-and-conquer matrix-vector multiplication. We implemented Ztune to autotune serial divide-and-conquer matrix-vector multiplication on machines with different hardware configurations, and found that Ztuneoptimized codes ran 1%-5% faster than the hand-optimized counterparts. We also compared Ztune-optimized results with other matrix-vector multiplication libraries including the Intel Math Kernel Library and OpenBLAS. Since the matrix-vector multiplication problem is a level 2 BLAS, it is not as computationally intensive as level 3 BLAS problems such as matrix-matrix multiplication and stencil computation. As a result, the measurement in matrix-vector multiplication is more prone to error from factors such as noise, cache alignment of the matrix, and cache states, which lead to wrong decision choices for Ztune. We explored multiple options to get more accurate measurements and demonstrated the techniques that remedied these issues. Lastly, we applied the Ztune approach to matrix-matrix multiplication, and we were able to achieve 2%-85% speedup compared to the hand-tuned code. This thesis represents joint work with Ekanathan Palamadai Natarajan.
by Payut Pantawongdecha.
M. Eng.
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10

Belgin, Mehmet. "Structure-based Optimizations for Sparse Matrix-Vector Multiply." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/30260.

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Анотація:
This dissertation introduces two novel techniques, OSF and PBR, to improve the performance of Sparse Matrix-vector Multiply (SMVM) kernels, which dominate the runtime of iterative solvers for systems of linear equations. SMVM computations that use sparse formats typically achieve only a small fraction of peak CPU speeds because they are memory bound due to their low flops:byte ratio, they access memory irregularly, and exhibit poor ILP due to inefficient pipelining. We particularly focus on improving the flops:byte ratio, which is the main limiter on performance, by exploiting recurring structures or sub-structures in matrices. Our techniques also support micro-architecture level optimizations to further improve performance. Operation Stacking Framework (OSF) stacks problems in large ensemble computations, which run the same sparse kernel using an identical matrix structure, such that they share a single copy of the indexing information to significantly reduce memory bandwidth usage. OSF provides performance improvements of up to 1.94x on an AMD Opteron compared to the CSR method. We validate performance results using hardware event counters, which demonstrate significantly improved cache and pipeline utilization. Pattern-based Representation (PBR) exploits recurring block nonzero patterns by generating custom code for each recurring block pattern. In this way, no indexing data for individual nonzero elements are read from memory, reducing the overall size of the indices by up to 98%. Our code generator emits highly tuned codes that utilize SSE vectorization and software prefetching. PBR accurately identifies a block size that achieves optimal or near-optimal performance using a linear multiple regression performance model. On recent multicore machines, PBR provides performance improvements of up to 3.4x sequentially and 5x in parallel, compared to the CSR method. The PBR library we provide converts matrices at runtime, allowing our method to be used as a drop-in replacement for existing methods. We compare PBRâ s overhead relative to its benefits and show that PBR is beneficial for many applications that repetitively call the SMVM kernel for the same matrix structure.
Ph. D.
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Книги з теми "Sparse Matrix Vector Multiplication"

1

Andersen, J. The scheduling of sparse matrix-vector multiplicatiion on a massively parallel DAP computer. Uxbridge: Brunel University, Department of Mathematics and Statistics, 1991.

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2

Itai, Yad-Shalom, and Langley Research Center, eds. Fast multiresolution algorithms for matrix-vector multiplication. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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3

United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., ed. An efficient sparse matrix multiplication scheme for the CYBER 205 computer. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Division, 1988.

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4

Bisseling, Rob H. Parallel Scientific Computation. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198788348.001.0001.

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Анотація:
This book explains how to use the bulk synchronous parallel (BSP) model to design and implement parallel algorithms in the areas of scientific computing and big data. Furthermore, it presents a hybrid BSP approach towards new hardware developments such as hierarchical architectures with both shared and distributed memory. The book provides a full treatment of core problems in scientific computing and big data, starting from a high-level problem description, via a sequential solution algorithm to a parallel solution algorithm and an actual parallel program written in the communication library BSPlib. Numerical experiments are presented for parallel programs on modern parallel computers ranging from desktop computers to massively parallel supercomputers. The introductory chapter of the book gives a complete overview of BSPlib, so that the reader already at an early stage is able to write his/her own parallel programs. Furthermore, it treats BSP benchmarking and parallel sorting by regular sampling. The next three chapters treat basic numerical linear algebra problems such as linear system solving by LU decomposition, sparse matrix-vector multiplication (SpMV), and the fast Fourier transform (FFT). The final chapter explores parallel algorithms for big data problems such as graph matching. The book is accompanied by a software package BSPedupack, freely available online from the author’s homepage, which contains all programs of the book and a set of test programs.
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Частини книг з теми "Sparse Matrix Vector Multiplication"

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Vassiliadis, Stamatis, Sorin Cotofana, and Pyrrhos Stathis. "Vector ISA Extension for Sparse Matrix-Vector Multiplication." In Euro-Par’99 Parallel Processing, 708–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48311-x_100.

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Maeda, Hiroshi, and Daisuke Takahashi. "Parallel Sparse Matrix-Vector Multiplication Using Accelerators." In Computational Science and Its Applications – ICCSA 2016, 3–18. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42108-7_1.

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Hishinuma, Toshiaki, Hidehiko Hasegawa, and Teruo Tanaka. "SIMD Parallel Sparse Matrix-Vector and Transposed-Matrix-Vector Multiplication in DD Precision." In High Performance Computing for Computational Science – VECPAR 2016, 21–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61982-8_4.

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Monakov, Alexander, and Arutyun Avetisyan. "Implementing Blocked Sparse Matrix-Vector Multiplication on NVIDIA GPUs." In Lecture Notes in Computer Science, 289–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03138-0_32.

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AlAhmadi, Sarah, Thaha Muhammed, Rashid Mehmood, and Aiiad Albeshri. "Performance Characteristics for Sparse Matrix-Vector Multiplication on GPUs." In Smart Infrastructure and Applications, 409–26. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13705-2_17.

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Çatalyürek, Ümit V., and Cevdet Aykanat. "Decomposing irregularly sparse matrices for parallel matrix-vector multiplication." In Parallel Algorithms for Irregularly Structured Problems, 75–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0030098.

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Wellein, Gerhard, Georg Hager, Achim Basermann, and Holger Fehske. "Fast Sparse Matrix-Vector Multiplication for TeraFlop/s Computers." In Lecture Notes in Computer Science, 287–301. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36569-9_18.

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Monakov, Alexander, Anton Lokhmotov, and Arutyun Avetisyan. "Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures." In High Performance Embedded Architectures and Compilers, 111–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11515-8_10.

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Vuduc, Richard W., and Hyun-Jin Moon. "Fast Sparse Matrix-Vector Multiplication by Exploiting Variable Block Structure." In High Performance Computing and Communications, 807–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11557654_91.

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Wijs, Anton J., and Dragan Bošnački. "Improving GPU Sparse Matrix-Vector Multiplication for Probabilistic Model Checking." In Model Checking Software, 98–116. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31759-0_9.

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Тези доповідей конференцій з теми "Sparse Matrix Vector Multiplication"

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Zhuo, Ling, and Viktor K. Prasanna. "Sparse Matrix-Vector multiplication on FPGAs." In the 2005 ACM/SIGDA 13th international symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1046192.1046202.

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Haque, Sardar Anisul, Shahadat Hossain, and Marc Moreno Maza. "Cache friendly sparse matrix-vector multiplication." In the 4th International Workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1837210.1837238.

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Shah, Monika. "Sparse Matrix Sparse Vector Multiplication - A Novel Approach." In 2015 44th International Conference on Parallel Processing Workshops (ICPPW). IEEE, 2015. http://dx.doi.org/10.1109/icppw.2015.18.

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Buluç, Aydin, Jeremy T. Fineman, Matteo Frigo, John R. Gilbert, and Charles E. Leiserson. "Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks." In the twenty-first annual symposium. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1583991.1584053.

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Zhuowei Wang, Xianbin Xu, Wuqing Zhao, Yuping Zhang, and Shuibing He. "Optimizing sparse matrix-vector multiplication on CUDA." In 2010 2nd International Conference on Education Technology and Computer (ICETC 2010). IEEE, 2010. http://dx.doi.org/10.1109/icetc.2010.5529724.

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Pinar, Ali, and Michael T. Heath. "Improving performance of sparse matrix-vector multiplication." In the 1999 ACM/IEEE conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/331532.331562.

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Sun, Junqing, Gregory Peterson, and Olaf Storaasli. "Sparse Matrix-Vector Multiplication Design on FPGAs." In 15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2007). IEEE, 2007. http://dx.doi.org/10.1109/fccm.2007.56.

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Merrill, Duane, and Michael Garland. "Merge-Based Parallel Sparse Matrix-Vector Multiplication." In SC16: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2016. http://dx.doi.org/10.1109/sc.2016.57.

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Li, Haoran, Harumichi Yokoyama, and Takuya Araki. "Merge-Based Parallel Sparse Matrix-Sparse Vector Multiplication with a Vector Architecture." In 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2018. http://dx.doi.org/10.1109/hpcc/smartcity/dss.2018.00038.

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Azad, Ariful, and Aydin Buluc. "A Work-Efficient Parallel Sparse Matrix-Sparse Vector Multiplication Algorithm." In 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2017. http://dx.doi.org/10.1109/ipdps.2017.76.

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Звіти організацій з теми "Sparse Matrix Vector Multiplication"

1

Vuduc, R., and H. Moon. Fast sparse matrix-vector multiplication by exploiting variable block structure. Office of Scientific and Technical Information (OSTI), July 2005. http://dx.doi.org/10.2172/891708.

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Deveci, Mehmet, Christian Robert Trott, and Sivasankaran Rajamanickam. Multi-threaded Sparse Matrix Sparse Matrix Multiplication for Many-Core and GPU Architectures. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1417260.

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Nusbaum, Kurtis Lee. Optimizing Tpetra%3CU%2B2019%3Es sparse matrix-matrix multiplication routine. Office of Scientific and Technical Information (OSTI), August 2011. http://dx.doi.org/10.2172/1029781.

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Deveci, Mehmet, Simon David Hammond, Michael M. Wolf, and Sivasankaran Rajamanickam. Sparse Matrix-Matrix Multiplication on Multilevel Memory Architectures: Algorithms and Experiments. Office of Scientific and Technical Information (OSTI), April 2018. http://dx.doi.org/10.2172/1435688.

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Hendrickson, B., R. Leland, and S. Plimpton. An efficient parallel algorithm for matrix-vector multiplication. Office of Scientific and Technical Information (OSTI), March 1993. http://dx.doi.org/10.2172/6519330.

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Liberty, Edo, and Steven W. Zucker. The Mailman Algorithm: A Note on Matrix Vector Multiplication. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada481737.

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Ballard, Grey Malone, Jonathan Joseph Hu, and Christopher Siefert. Reducing Communication Costs for Sparse Matrix Multiplication within Algebraic Multigrid. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1504845.

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Gropp, W. D., D. K. Kaushik, M. Minkoff, and B. F. Smith. Improving the performance of tensor matrix vector multiplication in quantum chemistry codes. Office of Scientific and Technical Information (OSTI), May 2008. http://dx.doi.org/10.2172/928654.

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Tolleson, Blayne, Matthew Marinella, Christopher Bennett, Hugh Barnaby, Donald Wilson, and Jesse Short. Vector-Matrix Multiplication Engine for Neuromorphic Computation with a CBRAM Crossbar Array. Office of Scientific and Technical Information (OSTI), February 2022. http://dx.doi.org/10.2172/1846087.

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Hammond, Simon David, and Christian Robert Trott. Optimizing the Performance of Sparse-Matrix Vector Products on Next-Generation Processors. Office of Scientific and Technical Information (OSTI), June 2017. http://dx.doi.org/10.2172/1528773.

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