Pipeline Scheduling Based on Constructive Interference in Strip Wireless Sensor Networks

Chang, Xiangmao; Xu, Xiaoxiang; Yang, Deliang

doi:10.32604/cmc.2020.08183

Cited by 3 publications

(1 citation statement)

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“…For several decades, scheduling and job scheduling have received constant attention from both researchers and practitioners from across the world. Such a level of interest is justified by the broad applications of scheduling which go from production planning and manufacturing [1,2], construction [3], cloud computing [4,5], transportation [6], education [7], healthcare [8] to CPUs [9], Internet of Things [10] and sensor networks [11]. Moreover, many scheduling problems are NP-hard, increasing the importance of developing faster and more efficient solving methods for scheduling problems with real-life instance sizes.…”

Section: Introductionmentioning

confidence: 99%

Parallel Makespan Calculation for Flow Shop Scheduling Problem with Minimal and Maximal Idle Time

Rudy

2021

Applied Sciences

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In this paper, a flow shop scheduling problem with minimal and maximal machine idle time with the goal of minimizing makespan is considered. The mathematical model of the problem is presented. A generalization of the prefix sum, called the job shift scan, for computing required shifts for overlapping jobs is proposed. A work-efficient algorithm for computing the job shift scan in parallel for the PRAM model with n processors is proposed and its time complexity of O(logn) is proven. Then, an algorithm for computing the makespan in time O(mlogn) in parallel using the prefix sum and job shift scan is proposed. Computer experiments on GPU were conducted using the CUDA platform. The results indicate multi-thread GPU vs. single-thread GPU speedups of up to 350 and 1000 for job shift scan and makespan calculation algorithms, respectively. Multi-thread GPU vs. single-thread CPU speedups up to 4.5 and 14.7, respectively, were observed as well. The experiments on the Taillard-based problem instances using a simulated annealing solving method and employing the parallel makespan calculation show that the method is able to perform many more iterations in the given time limit and obtain better results than the non-parallel version.

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