Square Matrix Multiplication Using CUDA on GP-GU

Jimale, Ali; Ridzuan, Fakhitah; Zainon, Wan Mohd Nazmee Wan

doi:10.1016/j.procs.2019.11.138

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Matrix multiplication is the most time-consuming task when training a large dataset. To minimize computing time and to accelerate the processes during preparation, a parallelized matrix multiplication algorithm has been used [13].…”

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confidence: 99%

Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction

Singh

Panda

2022

IJECE

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<p>A multitude of research has been rising for predicting the behavior of different real-world problems through machine learning models. An erratic nature occurs due to the augmented behavior and inadequacy of the prerequisite dataset for the prediction of water level over different fundamental models that show flat or low-set accuracy. In this paper, a powerful scaling strategy is proposed for improvised back-propagation algorithm using parallel computing for groundwater level prediction on graphical processing unit (GPU) for the Faridabad region, Haryana, India. This paper aims to propose the new streamlined form of a back-propagation algorithm for heterogeneous computing and to examine the coalescence of artificial neural network (ANN) with GPU for predicting the groundwater level. twenty years of data set from 2001-2020 has been taken into consideration for three input parameters namely, temperature, rainfall, and water level for predicting the groundwater level using parallelized backpropagation algorithm on compute unified device architecture (CUDA). This employs the back-propagation algorithm to be best suited to reinforce learning and performance by providing more accurate and fast results for water level predictions on GPUs as compared to sequential ones on central processing units (CPUs) alone.</p>

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confidence: 99%