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Implementation of the Spark technique in a matrix distributed computing algorithm
2022
Journal of Intelligent Systems
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Two analyzes of Spark engine performance strategies to implement the Spark technique in a matrix distributed computational algorithm, the multiplication of a sparse multiplication operational test model ...
with the relationship between the sparse matrix multiplication implementation in the single-machine sparse matrix test and the computational performance of the local native library. ...
Results and analysis 4.1 Experimental design and result analysis of distributed sparse-sparse matrix multiplication operation First, fix the dimensions of the two input sparse matrices to be 30,000 × 30,000 ...
doi:10.1515/jisys-2022-0051
fatcat:pagkmfd4ezbcdoqyomelkz7qu4
An Effective Compact Representation Model for Big Sparse Matrix Multiplication
2018
International Journal of Engineering & Technology
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Finally we established that the new algorithm performs well in sparse big data scenario compared to the existing techniques in big data processing. ...
It applies compact representation techniques of sparse data and moulds the required data in the mapreducible format. ...
Table 1 shows comparative analysis of Big-RANGEsparseMUL with existing matrix multiplication approaches in the big sparse data scenario. ...
doi:10.14419/ijet.v7i4.10.20827
fatcat:ws3trxwjyrfj3g27nhl2w6g6ai
Sparse Matrix Multiplication On An Associative Processor
2015
IEEE Transactions on Parallel and Distributed Systems
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Four sparse matrix multiplication algorithms are explored in this paper, combining AP and CPU processing to various levels. They are evaluated by simulation on a large set of sparse matrices. ...
Sparse matrix multiplication is an important component of linear algebra computations. ...
In this work, we present four different algorithms of sparse matrix-matrix multiplication on the AP. ...
doi:10.1109/tpds.2014.2370055
fatcat:2sp6aswv7ra3ff6vfp5i6wbyt4
Highly Parallel Sparse Matrix-Matrix Multiplication
[article]
2010
arXiv
pre-print
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Generalized sparse matrix-matrix multiplication is a key primitive for many high performance graph algorithms as well as some linear solvers such as multigrid. ...
Our algorithms are based on two-dimensional block distribution of sparse matrices where serial sections use a novel hypersparse kernel for scalability. ...
Although they achieve reasonable performance on some classes of matrices, none of these algorithms outperforms the classical sparse matrix-matrix multiplication algorithm for general sparse matrices, which ...
arXiv:1006.2183v1
fatcat:ej4x646fvnatzjd5z2e2uodtau
Page 40 of Journal of Research and Practice in Information Technology Vol. 27, Issue 2
[page]
1995
Journal of Research and Practice in Information Technology
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The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
Ss SPARSE MATRIX MULTIPLICATION ON A RECONFIGURABLE MES <A
element on their north-south bus. ...
With respect to the multiplication of arbitrary matrices A and B our algorithm will be superior to standard systolic matrix multiplication if the number of nonzero ele- ments per row and column of A and ...
Parallel Sparse Matrix-Matrix Multiplication and Indexing: Implementation and Experiments
2012
SIAM Journal on Scientific Computing
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Generalized sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many high performance graph algorithms as well as for some linear solvers, such as algebraic multigrid. ...
Here we show that SpGEMM also yields efficient algorithms for general sparse-matrix indexing in distributed memory, provided that the underlying SpGEMM implementation is sufficiently flexible and scalable ...
Fig. 3 . 4 . 34 Matrix A in DCSC format.
Fig. 3 . 5 . 35 Execution of the Sparse SUMMA algorithm for sparse matrix-matrix multiplication C = A · B. ...
doi:10.1137/110848244
fatcat:eedmnqz2tbcqfmbjqjmokp7i5e
Page 5299 of Mathematical Reviews Vol. , Issue 82m
[page]
1982
Mathematical Reviews
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Also the new techniques allow us to simplify the
design of the fastest known APA-algorithms for matrix multiplication.” ...
Finally, the multiplication methods for sparse Boolean matrices are extended to work with general sparse matrices with the same time complexity.”
65G_ Error analysis See also 65050, 68014. ...
An efficient classification approach for large-scale mobile ubiquitous computing
2013
Information Sciences
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Firstly, we propose a semi-sparse algorithm to speed up vector/matrix multiplication which lies at the core of the SVMTorch-based classification approaches. ...
by inefficient matrix multiplication, and (2) the inability to classify multi-label data. ...
Based on this analysis, we propose a new semi-sparse algorithm to speed up the matrix multiplication. ...
doi:10.1016/j.ins.2012.09.050
fatcat:guz4kscvkbekbdxczqetlsmwvy
A note on the multiplication of sparse matrices
2014
Open Computer Science
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AbstractWe present a practical algorithm for multiplication of two sparse matrices. ...
Finally a comparison of number of required multiplications in the naïve matrix multiplication, Strassen's method and our algorithm is given. ...
The first author is also thankful to the National Elite Foundation of Iran for partial financial support. ...
doi:10.2478/s13537-014-0201-x
fatcat:pxjukuavuzf2zbppdyrxfrhouq
Reducing Inter-Process Communication Overhead in Parallel Sparse Matrix-Matrix Multiplication
2017
International Journal of Grid and High Performance Computing
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Parallel sparse matrix-matrix multiplication algorithms (PSpGEMM) spend most of their running time on interprocess communication. ...
This overhead can be reduced with a one dimensional distributed algorithm for parallel sparse matrix-matrix multiplication that uses a novel accumulation pattern based on logarithmic complexity of the ...
A very few classical algorithms describe the communication cost of sparse matrix-matrix multiplication. ...
doi:10.4018/ijghpc.2017070104
fatcat:ko6pzpcnofbihftoy7cf2ztkmi
Graphs, Matrices, and the GraphBLAS: Seven Good Reasons
2015
Procedia Computer Science
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Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. ...
The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. ...
Theoretical Analysis While matrix-based graph approaches have been around since the inception of graph theory, these approaches were less widely used for graph algorithm analysis. ...
doi:10.1016/j.procs.2015.05.353
fatcat:kys7obcdqbdqbaoiffiuqlqzry
Split: a flexible and efficient algorithm to vector-descriptor product
2007
Proceedings of the 2nd International ICST Conference on Performance Evaluation Methodologies and Tools
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of a traditional sparse matrix. ...
One of the most efficient algorithms used to compute iterative solutions of descriptors is the Shuffle algorithm which is used to perform the multiplication by a probability vector. ...
Such variant demands the smaller number of floating point multiplications (Equation 3 ), but demands the storage of a sparse matrix with Q N i=1 nzi nonzero elements. ...
doi:10.4108/smctools.2007.1982
dblp:conf/valuetools/CzeksterFVW07
fatcat:jii475x5evg6hakor4d6arczqa
Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs
2017
IEEE Transactions on Parallel and Distributed Systems
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2016 pre-print arXiv:1602.02864v2 fatcat:tql4d54qhbehtdctzsa24moug4Other Versions
Sparse matrix multiplication is traditionally performed in memory and scales to large matrices using the distributed memory of multiple nodes. ...
In contrast, we scale sparse matrix multiplication beyond memory capacity by implementing sparse matrix dense matrix multiplication (SpMM) in a semi-external memory (SEM) fashion; i.e., we keep the sparse ...
Graph analysis algorithms such as PageRank [8] can be formulated as sparse matrix multiplication or generalized sparse matrix multiplication [24] . ...
doi:10.1109/tpds.2016.2618791
fatcat:n7fc34xn4rbmfgoqhuz5tiedjy
Fast Matrix Multiplication with Big Sparse Data
2017
Cybernetics and Information Technologies
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Big Data becameabuzz word nowadays due to the evolution of huge volumes of data beyond peta bytes. This article focuses on matrix multiplication with big sparse data. ...
The proposed FASTsparse MULalgorithm outperforms the state-of-the-art big matrix multiplication approaches in sparse data scenario. ...
sparse matrices Pre-processing overhead Mainly intended for sparse matrices multiplication Application of the algorithm to dense matrices is yet to be studied Table 1 shows comparative analysis ...
doi:10.1515/cait-2017-0002
fatcat:7dvcgtkghzbf5b2lgrhcd322sq
Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format
[article]
2020
arXiv
pre-print
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Multiplication of a sparse matrix to a dense matrix (SpDM) is widely used in many areas like scientific computing and machine learning. ...
The storage data structures help sparse matrices store in a memory-saving format, but they bring difficulties in optimizing the performance of SpDM on modern GPUs due to irregular data access of the sparse ...
calculation to finish the matrix multiplication using the sparse algorithm. ...
arXiv:2005.14469v1
fatcat:42n64i7olvh33lcpgx2z6j4rvu
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