Fast and processor efficient parallel matrix multiplication algorithms on a linear array with a reconfigurable pipelined bus system

We present efficient parallel matrix multiplication algorithms for linear arrays with reconfigurable pipelined bus systems (LARPBS). Such systems are able to support a large volume of parallel communication of various patterns in constant time. An LARPBS can also be reconfigured into many independen...

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Published inIEEE transactions on parallel and distributed systems Vol. 9; no. 8; pp. 705 - 720
Main Authors Li, Keqin, Pan, Yi, Zheng, Si Qing
Format Journal Article
LanguageEnglish
Published IEEE 01.08.1998
Subjects
Online AccessGet full text
ISSN1045-9219
DOI10.1109/71.706044

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Abstract We present efficient parallel matrix multiplication algorithms for linear arrays with reconfigurable pipelined bus systems (LARPBS). Such systems are able to support a large volume of parallel communication of various patterns in constant time. An LARPBS can also be reconfigured into many independent subsystems and, thus, is able to support parallel implementations of divide-and-conquer computations like Strassen's algorithm. The main contributions of the paper are as follows. We develop five matrix multiplication algorithms with varying degrees of parallelism on the LARPBS computing model; namely, MM/sub 1/, MM/sub 2/, MM/sub 3/, and compound algorithms C/sub 1/(/spl epsiv/)and C/sub 2/(/spl delta/). Algorithm C/sub 1/(/spl epsiv/) has adjustable time complexity in sublinear level. Algorithm C/sub 2/(/spl delta/) implies that it is feasible to achieve sublogarithmic time using /spl sigma/(N/sup 3/) processors for matrix multiplication on a realistic system. Algorithms MM/sub 3/, C/sub 1/(/spl epsiv/), and C/sub 2/(/spl delta/) all have o(/spl Nscr//sup 3/) cost and, hence, are very processor efficient. Algorithms MM/sub 1/, MM/sub 3/, and C/sub 1/(/spl epsiv/) are general-purpose matrix multiplication algorithms, where the array elements are in any ring. Algorithms MM/sub 2/ and C/sub 2/(/spl delta/) are applicable to array elements that are integers of bounded magnitude, or floating-point values of bounded precision and magnitude, or Boolean values. Extension of algorithms MM/sub 2/ and C/sub 2/(/spl delta/) to unbounded integers and reals are also discussed.
AbstractList We present efficient parallel matrix multiplication algorithms for linear arrays with reconfigurable pipelined bus systems (LARPBS). Such systems are able to support a large volume of parallel communication of various patterns in constant time. An LARPBS can also be reconfigured into many independent subsystems and, thus, is able to support parallel implementations of divide-and-conquer computations like Strassen's algorithm. The main contributions of the paper are as follows: We develop five matrix multiplication algorithms with varying degrees of parallelism on the LARPBS computing model, namely, MM sub(1), MM sub(2), MM sub(3), and compound algorithms C sub(1)( epsilon ) and C sub(2)( delta ). Algorithm C sub(1)( epsilon ) has adjustable time complexity in sublinear level. Algorithm C sub(2)( delta ) implies that it is feasible to achieve sublogarithmic time using o(N super(3)) processors for matrix multiplication on a realistic system. Algorithms MM sub(3), C sub(1)( epsilon ), and C sub(2)( delta ) all have o(N super(3)) cost and, hence, are very processor efficient. Algorithms MM sub(1), MM sub(3), and C sub(1)( epsilon ) are general-purpose matrix multiplication algorithms, where the array elements are in any ring. Algorithms MM sub(2) and C sub(2)( delta ) are applicable to array elements that are integers of bounded magnitude, or floating-point values of bounded precision and magnitude, or Boolean values. Extension of algorithms MM sub(2) and C sub(2)( delta ) to unbounded integers and reals are also discussed.
We present efficient parallel matrix multiplication algorithms for linear arrays with reconfigurable pipelined bus systems (LARPBS). Such systems are able to support a large volume of parallel communication of various patterns in constant time. An LARPBS can also be reconfigured into many independent subsystems and, thus, is able to support parallel implementations of divide-and-conquer computations like Strassen's algorithm. The main contributions of the paper are as follows. We develop five matrix multiplication algorithms with varying degrees of parallelism on the LARPBS computing model; namely, MM(1), MM (2), MM(3), and compound algorithms C(1)(epsilon)and C(2)(delta). Algorithm C(1)(epsilon) has adjustable time complexity in sublinear level. Algorithm C(2)(delta) implies that it is feasible to achieve sublogarithmic time using sigma(N(3)) processors for matrix multiplication on a realistic system. Algorithms MM(3), C(1)(epsilon), and C(2)(delta) all have o( (3)) cost and, hence, are very processor efficient. Algorithms MM(1), MM(3), and C(1)(epsilon) are general-purpose matrix multiplication algorithms, where the array elements are in any ring. Algorithms MM(2) and C(2)(delta) are applicable to array elements that are integers of bounded magnitude, or floating-point values of bounded precision and magnitude, or Boolean values. Extension of algorithms MM (2) and C(2)(delta) to unbounded integers and reals are also discussed
We present efficient parallel matrix multiplication algorithms for linear arrays with reconfigurable pipelined bus systems (LARPBS). Such systems are able to support a large volume of parallel communication of various patterns in constant time. An LARPBS can also be reconfigured into many independent subsystems and, thus, is able to support parallel implementations of divide-and-conquer computations like Strassen's algorithm. The main contributions of the paper are as follows. We develop five matrix multiplication algorithms with varying degrees of parallelism on the LARPBS computing model; namely, MM/sub 1/, MM/sub 2/, MM/sub 3/, and compound algorithms C/sub 1/(/spl epsiv/)and C/sub 2/(/spl delta/). Algorithm C/sub 1/(/spl epsiv/) has adjustable time complexity in sublinear level. Algorithm C/sub 2/(/spl delta/) implies that it is feasible to achieve sublogarithmic time using /spl sigma/(N/sup 3/) processors for matrix multiplication on a realistic system. Algorithms MM/sub 3/, C/sub 1/(/spl epsiv/), and C/sub 2/(/spl delta/) all have o(/spl Nscr//sup 3/) cost and, hence, are very processor efficient. Algorithms MM/sub 1/, MM/sub 3/, and C/sub 1/(/spl epsiv/) are general-purpose matrix multiplication algorithms, where the array elements are in any ring. Algorithms MM/sub 2/ and C/sub 2/(/spl delta/) are applicable to array elements that are integers of bounded magnitude, or floating-point values of bounded precision and magnitude, or Boolean values. Extension of algorithms MM/sub 2/ and C/sub 2/(/spl delta/) to unbounded integers and reals are also discussed.
Author Si Qing Zheng
Keqin Li
Yi Pan
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Snippet We present efficient parallel matrix multiplication algorithms for linear arrays with reconfigurable pipelined bus systems (LARPBS). Such systems are able to...
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SubjectTerms Concurrent computing
Costs
Eigenvalues and eigenfunctions
Graph theory
Optical arrays
Parallel algorithms
Parallel processing
Polynomials
Power engineering and energy
Tree graphs
Title Fast and processor efficient parallel matrix multiplication algorithms on a linear array with a reconfigurable pipelined bus system
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