Laleh Ghalami

Wayne State University, Computer Science, Graduate Student

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Papers by Laleh Ghalami

A Family of Fast Parallel Greedy Algorithms for the Steiner Forest Problem

2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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A GPU Parallel Approximation Algorithm for Scheduling Parallel Identical Machines to Minimize Makespan

2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018

We present a parallel approximation algorithm for the problem of scheduling jobs on parallel iden... more We present a parallel approximation algorithm for the problem of scheduling jobs on parallel identical machines to minimize makespan which is designed and optimized for running efficiently on the GPU. The algorithm is a Polynomial Time Approximation Scheme (PTAS) based on a higher-dimensional dynamic programming approach, where dimensionality refers to the number of variables in the dynamic programming equation characterizing the problem. The main component of our design consists of a novel data-partitioning technique that is employed to accelerate the higher-dimensional dynamic programming component of the algorithm. We present performance results to demonstrate how our proposed design improves the GPU utilization and makes it possible to solve large higher-dimensional dynamic programming problems with the limited GPU memory. Experimental results show that the GPU implementation outperforms the optimized OpenMP implementation of the approximation algorithm.

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A Parallel Approximation Algorithm for Scheduling Parallel Identical Machines

2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017

We present the design and analysis of a parallel approximation algorithm for the problem of sched... more We present the design and analysis of a parallel approximation algorithm for the problem of scheduling jobs on parallel identical machines to minimize makespan. The design of the parallel approximation algorithm is based on the best existing polynomial-time approximation scheme (PTAS) for the problem. To the best of our knowledge, this is the first practical parallel approximation algorithm for the minimum makespan scheduling problem that maintains the approximation guarantees of the sequential PTAS and it is specifically designed for execution on shared-memory parallel machines. We implement and run the algorithm on a multi-core system and perform an extensive experimental analysis. The results show that our proposed parallel approximation algorithm achievessignificant speedup with respect to both the sequential PTAS and the CPLEX-basedsolver that solve the integer program formulation of the problem.

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Approximation algorithms for Steiner forest: An experimental study

Networks, 2021

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Scheduling parallel identical machines to minimize makespan:A parallel approximation algorithm

Journal of Parallel and Distributed Computing, 2018

Approximation algorithms for scheduling parallel machines have been studied for decades, leading ... more Approximation algorithms for scheduling parallel machines have been studied for decades, leading to significant progress in terms of their approximation guarantees. The algorithms that provide near optimal performance are not feasible to use in practice due to their huge execution time requirements, thus underscoring the importance of developing efficient parallel approximation algorithms with near-optimal performance guarantees that are suitable for execution on current parallel systems, such as multi-core systems. We present the design and analysis of a parallel approximation algorithm for the problem of scheduling jobs on parallel identical machines to minimize makespan. The design of the parallel approximation algorithm is based on the best existing polynomial-time approximation scheme (PTAS) for the problem. To the best of our knowledge, this is the first practical parallel approximation algorithm for the minimum makespan scheduling problem that maintains the approximation guarantees of the sequential PTAS and it is specifically designed for execution on sharedmemory parallel machines. We implement and run the algorithm on a large multi-core system and perform an extensive experimental analysis on data generated from realistic probability distributions. The results show that our proposed parallel approximation algorithm achieves significant speedup with respect to both the sequential PTAS and the CPLEX-based solver that solves the mixed integer program formulation of the problem.

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On linear programming relaxations for solving polynomial programming problems

Computers & Operations Research, 2018

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights • Theoretically compared the tightness of the J-set relaxation with the recursive McCormick relaxations • Analyzed the computational tractability of the J-set and the recursive McCormick relaxations • Neither formulation has uniform superiority when implemented within a branch-and-bound framework. • We suggest J-set relaxation for degree ≤ 6 and enhanced McCormick relaxations for degree ≥ 7

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A Parallel Approximation Algorithm for the Steiner Forest Problem

2022 30th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)

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