Results 41 to 50 of about 53,358 (305)
IEEE Access, 2020 Matrix computing is a basic operational model that was broadly used in science and engineering applications. In this study, we first propose a novel optimization method to obtain a high-performance and scalable architecture for matrix multiplication ...
Longlong Zhang +3 more
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Accelerating Batched Matrix Multiplication for Variable Small Sizes Based on TVM andApplications [PDF]
Jisuanji kexueIn many practical applications,efficient computation of a large amount of small matrix products across different dimensions is required.For instance,in graph classification tasks based on graph neural networks,multiple adjacency matrices need to be ...
DAI Hanwen, CHEN Changbo
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Development an Analytical Performance Models for Matrix Multiplication on Distributing Systems [PDF]
IJCI International Journal of Computers and Information, 2009 In this paper, we suggest a mechanism for implementing a distributed application using RMI based on JAVA threads. The application is parallel matrices multiplication depending on distributed the products block of rows and columns on different machines ...
Arabi Keshk
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Fully parallel optical matrix-matrix multiplication
, 2023 In recent years, with the rapid development of electro-optic modulators, optical computing has become a potential excellent candidate for various computing tasks.
Kaizhi Wang (13997284) +2 more
core +1 more source
On generalized corners and matrix multiplication
CoRR, 2023 Suppose that $S \subseteq [n]^2$ contains no three points of the form $(x,y), (x,y+δ), (x+δ,y')$, where $δ\neq 0$. How big can $S$ be? Trivially, $n \le |S| \le n^2$. Slight improvements on these bounds are obtained from Shkredov's upper bound for the corners problem [Shk06], which shows that $|S| \le O(n^2/(\log \log n)^c)$ for some small $c > 0 ...
openaire +4 more sources
A New Parallel Matrix Multiplication Method Adapted on Fibonacci Hypercube Structure [PDF]
Journal of Sciences, Islamic Republic of Iran, 2010 The objective of this study was to develop a new optimal parallel algorithm for matrix multiplication which could run on a Fibonacci Hypercube structure. Most of the popular algorithms for parallel matrix multiplication can not run on Fibonacci Hypercube
L Jokar
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Multiplication of medium-density matrices using TensorFlow on multicore CPUs
Tehnički Glasnik, 2019 Matrix multiplication is an essential part of many applications, such as linear algebra, image processing and machine learning. One platform used in such applications is TensorFlow, which is a machine learning library whose structure is based on dataflow
Siraphob Theeracheep +1 more
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Hyper-systolic matrix multiplication [PDF]
Parallel Computing, 2001 A novel parallel algorithm for matrix multiplication is presented. The hyper-systolic algorithm makes use of a one-dimensional processor abstraction. The procedure can be implemented on all types of parallel systems. It can handle matrix-vector multiplications as well as transposed matrix products.
Thomas Lippert +3 more
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Cogent Engineering, 2017 Matrix Multiplication is a basic engineering and scientific problem, which has application in various domains. There exists many cryptographic solutions for secure computation of matrix multiplication, but cryptographic preamble makes them infeasible for
Malay Kumar, Jasraj Meena, Manu Vardhan
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A practical streaming approximate matrix multiplication algorithm
Journal of King Saud University: Computer and Information Sciences, 2022 Approximate Matrix Multiplication (AMM) has emerged as a useful and computationally inexpensive substitute for actual multiplication of large matrices. Randomized as well as deterministic solutions to AMM were provided in the past.
Deena P. Francis, Kumudha Raimond
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