Parallel multiplication

Yuen, C. K.; Feng, Ming-Dong

doi:10.1145/181587.181589

Cited by 2 publications

(2 citation statements)

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“…The second direction has been to reduce the cost of multiplying the data set by proposing an efficient strategy to multiply the ݉ numbers. Regarding the first research direction, many methods have been proposed to reduce the time complexity of multiplying two integers in both sequential [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] and parallel computation [24][25][26][27][28][29]. In the case of sequential computation, several techniques have been proposed such as the Naïve multiplication algorithm [1], Karatsuba's algorithm [1,15], the Toom-Cook multiplication algorithm [20], and a fast Fourier transform-based algorithm [18].…”

Section: Introductionmentioning

confidence: 99%

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Speeding up the Multiplication Algorithm for Large Integers

Bahig

AlGhadhban

Mahdi

et al. 2020

Eng. Technol. Appl. Sci. Res.

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Multiplication is one of the basic operations that influence the performance of many computer applications such as cryptography. The main challenge of the multiplication operation is the cost of the operation as compared to other basic operations such as addition and subtraction, especially when the size of the numbers is large. In this work, we investigate the use of the window strategy for multiplying a sequence of large integers to design an efficient sequential algorithm in order to reduce the number of bit-multiplication operations involved in multiplying a sequence of large integers. In our implementation, several parameters are considered and measured for their effect on the proposed algorithm and the best-known sequential algorithm in the literature. These parameters are the size of the sequence of integers, the size of the integers, the size of the window, and the distribution of the data. The experimental results prove the effectiveness of the proposed algorithm are compared to the ones of the best-known sequential algorithm, and the proposed algorithm is able to achieve a reduction in computing time greater than 50% in most cases.

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Section: Introductionmentioning

confidence: 99%

“…On the other hand, many different attempts have been made to parallelize the multiplication problem using different parallel models [24][25][26][27][28][29]. Most of these attempts have been based on the shared memory model, where the processors in this model communicate through shared memory.…”

Section: Introductionmentioning

confidence: 99%