Results 211 to 220 of about 41,758 (261)

Threaded Accurate Matrix-Matrix Multiplications with Sparse Matrix-Vector Multiplications

2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018
Basic Linear Algebra Subprograms (BLAS) is a frequently used numerical library for linear algebra computations. However, it places little emphasis on computational accuracy, especially with respect to the accuracy assurance of the results. Although some algorithms for ensuring the computational accuracy of BLAS operations have been studied, there is a ...
Shuntaro Ichimura, Takahiro Katagiri, Katsuhisa Ozaki, Takeshi Ogita, Toru Nagai   +4 more
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Matrix-Matrix Multiplication Using Multiple GPUs Connected by Nvlink

2020 Global Smart Industry Conference (GloSIC), 2020
In this work we present an original GPU-only parallel matrix-matrix multiplication algorithm $(C = aA * B + \beta C)$ for servers with multiple GPUs connected by NVLink. The algorithm is implemented using CUDA. The data transfer patterns, the communication and computation overlap, and the overall performance of the algorithm are considered.
Yea Rem Choi, Vsevolod Nikolskiy, Vladimir Stegailov   +2 more
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Fault tolerant matrix-matrix multiplication

Proceedings of the second workshop on Scalable algorithms for large-scale systems, 2011
Soft errors are one-time events that corrupt the state of a computing system but not its overall functionality. Soft errors normally do not interrupt the execution of the affected program, but the affected computation results can not be trusted any more.
Panruo Wu, Chong Ding, Longxiang Chen, Feng Gao, Teresa Davies, Christer Karlsson, Zizhong Chen   +6 more
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Multiple BDD based matrix multiplication

2010 IEEE International Conference on Semiconductor Electronics (ICSE2010), 2010
Binary Decision Diagrams (BDDs) are the most frequently used data structure for handling Boolean functions because of their excellent efficiency in terms of time and space. Algebraic Decision Diagrams (ADDs) have been used to solve general purpose problems such as Matrix Multiplication, logic synthesis and Formal Verification. We propose a Multiple BDD
T. Bhuvaneswari, V. C. Prasad, Ajay Kumar Singh   +2 more
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Matrix multiplication with DNA

Journal of Molecular Evolution, 1997
A DNA-based method for calculating the product of Boolean matrices or matrices containing positive, real numbers is presented. In the case of matrices containing real numbers, the manipulation of reaction conditions allows a quantitative calculation to be performed. The use of DNA to perform an analog calculation illustrates a new approach to computing
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Visualizing Matrix Multiplication

PRIMUS, 2017
Efficient visualizations of computational algorithms are important tools for students, educators, and researchers.
Peteris Daugulis, Anita Sondore
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Performance Evaluation of Accurate Matrix-Matrix Multiplication on GPU Using Sparse Matrix Multiplications

2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW), 2020
Basic Linear Algebra Subprograms (BLAS) is a frequently used numerical library for linear algebra computations. However, it places little emphasis on computational accuracy, especially with respect to the accuracy assurance of the results. Consequently, a high-precision matrix–matrix multiplications algorithm that assures the precision by double ...
Fumiya Ishiguro, Takahiro Katagiri, Satoshi Ohshima, Toru Nagai   +3 more
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Avoiding matrix multiplication

1991
The fastest known algorithms for many problems on graphs use matrix multiplication as a sub-routine. Some examples of problems solved using matrix multiplication are recognition of transitive graphs, computing the transitive closure of a directed acyclic graph, and finding the neighborhood containment matrix of a graph.
Tze-Heng Ma, Jeremy P. Spinrad
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Fast matrix multiplication

Proceedings of the third annual ACM symposium on Theory of computing - STOC '71, 1971
This paper deals with three aspects of algebraic complexity. The first section is concerned with lower bounds on the number of operations required to compute several functions. Several theorems are presented and their proofs sketched. The second section deals with relationships among the complexities of several sets of functions.
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