scholarly journals GRASP and Iterated Local Search-Based Cellular Processing algorithm for Precedence-Constraint Task List Scheduling on Heterogeneous Systems

2020 ◽  
Vol 10 (21) ◽  
pp. 7500
Author(s):  
Alejandro Santiago ◽  
J. David Terán-Villanueva ◽  
Salvador Ibarra Martínez ◽  
José Antonio Castán Rocha ◽  
Julio Laria Menchaca ◽  
...  
Keyword(s):  
Local Search ◽  
High Performance ◽  
Heterogeneous Systems ◽  
Search Space ◽  
Iterated Local Search ◽  
Detection Mechanism ◽  
Task List ◽  
Np Hard Problem ◽  
Cellular Processing ◽  
The Individual

High-Performance Computing systems rely on the software’s capability to be highly parallelized in individual computing tasks. However, even with a high parallelization level, poor scheduling can lead to long runtimes; this scheduling is in itself an NP-hard problem. Therefore, it is our interest to use a heuristic approach, particularly Cellular Processing Algorithms (CPA), which is a novel metaheuristic framework for optimization. This framework has its foundation in exploring the search space by multiple Processing Cells that communicate to exploit the search and in the individual stagnation detection mechanism in the Processing Cells. In this paper, we proposed using a Greedy Randomized Adaptive Search Procedure (GRASP) to look for promising task execution orders; later, a CPA formed with Iterated Local Search (ILS) Processing Cells is used for the optimization. We assess our approach with a high-performance ILS state-of-the-art approach. Experimental results show that the CPA outperforms the previous ILS in real applications and synthetic instances.

scholarly journals Energy Idle Aware Stochastic Lexicographic Local Searches for Precedence-Constraint Task List Scheduling on Heterogeneous Systems

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3473
Author(s):  
Alejandro Santiago ◽  
Mirna Ponce-Flores ◽  
J. David Terán-Villanueva ◽  
Fausto Balderas ◽  
Salvador Ibarra Martínez ◽  
...  
Keyword(s):  
Energy Consumption ◽  
High Performance ◽  
Heterogeneous Systems ◽  
Parallel Applications ◽  
Literature Study ◽  
Task List ◽  
Local Searches ◽  
Np Hard Problem ◽  
Comparative Literature Study ◽  
Performance Computing

The use of parallel applications in High-Performance Computing (HPC) demands high computing times and energy resources. Inadequate scheduling produces longer computing times which, in turn, increases energy consumption and monetary cost. Task scheduling is an NP-Hard problem; thus, several heuristics methods appear in the literature. The main approaches can be grouped into the following categories: fast heuristics, metaheuristics, and local search. Fast heuristics and metaheuristics are used when pre-scheduling times are short and long, respectively. The third is commonly used when pre-scheduling time is limited by CPU seconds or by objective function evaluations. This paper focuses on optimizing the scheduling of parallel applications, considering the energy consumption during the idle time while no tasks are executing. Additionally, we detail a comparative literature study of the performance of lexicographic variants with local searches adapted to be stochastic and aware of idle energy consumption.

Using Iterated Local Search for Efficiently Packing Unequal Disks in a Larger Circle

2012 ◽  
Vol 430-432 ◽  
pp. 1477-1481 ◽  
Author(s):  
Wen Qi Huang ◽  
Zhi Zhong Zeng ◽  
Ru Chu Xu ◽  
Zhang Hua Fu
Keyword(s):  
Local Search ◽  
Iterated Local Search ◽  
Critical Element ◽  
Search Procedure ◽  
Hard Problem ◽  
Bfgs Method ◽  
Real World Applications ◽  
Local Search Procedure ◽  
Np Hard Problem ◽  
World Records

Packing unequal disks in a container as small as possible without mutual overlap is a NP-hard problem with a plenty of real world applications. In this paper, we introduced iterated local search to tackle with this problem, and using Critical Element Guided Perturbation (CEGP) strategy to jump out the local minimal, and using BFGS method to get each neighborhood optimized in local search procedure. Experiments showed its efficiency by breaking several world records of a set of benchmark.

Improving Efficiency in Robot Assisted Belt Grinding of High Performance Materials

2014 ◽  
Vol 907 ◽  
pp. 139-149 ◽  
Author(s):  
Eckart Uhlmann ◽  
Florian Heitmüller
Keyword(s):  
Gas Turbines ◽  
High Performance ◽  
Scale Effects ◽  
Turbine Blades ◽  
Repair Process ◽  
Industrial Robots ◽  
Robot Assisted ◽  
Belt Grinding ◽  
Economy Of Scale ◽  
The Individual

In gas turbines and turbo jet engines, high performance materials such as nickel-based alloys are widely used for blades and vanes. In the case of repair, finishing of complex turbine blades made of high performance materials is carried out predominantly manually. The repair process is therefore quite time consuming. And the costs of presently available repair strategies, especially for integrated parts, are high, due to the individual process planning and great amount of manually performed work steps. Moreover, there are severe risks of partial damage during manually conducted repair. All that leads to the fact that economy of scale effects remain widely unused for repair tasks, although the piece number of components to be repaired is increasing significantly. In the future, a persistent automation of the repair process chain should be achieved by developing adaptive robot assisted finishing strategies. The goal of this research is to use the automation potential for repair tasks by developing a technology that enables industrial robots to re-contour turbine blades via force controlled belt grinding.

Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software

2021 ◽  
Vol 47 (2) ◽  
pp. 1-28
Author(s):  
Goran Flegar ◽  
Hartwig Anzt ◽  
Terry Cojean ◽  
Enrique S. Quintana-Ortí
Keyword(s):  
Linear Algebra ◽  
Graphics Processing Units ◽  
High Performance ◽  
Numerical Algorithms ◽  
Mixed Precision ◽  
Before And After ◽  
Memory Accesses ◽  
Specialized Hardware ◽  
The Individual ◽  
Graphics Processing

The use of mixed precision in numerical algorithms is a promising strategy for accelerating scientific applications. In particular, the adoption of specialized hardware and data formats for low-precision arithmetic in high-end GPUs (graphics processing units) has motivated numerous efforts aiming at carefully reducing the working precision in order to speed up the computations. For algorithms whose performance is bound by the memory bandwidth, the idea of compressing its data before (and after) memory accesses has received considerable attention. One idea is to store an approximate operator–like a preconditioner–in lower than working precision hopefully without impacting the algorithm output. We realize the first high-performance implementation of an adaptive precision block-Jacobi preconditioner which selects the precision format used to store the preconditioner data on-the-fly, taking into account the numerical properties of the individual preconditioner blocks. We implement the adaptive block-Jacobi preconditioner as production-ready functionality in the Ginkgo linear algebra library, considering not only the precision formats that are part of the IEEE standard, but also customized formats which optimize the length of the exponent and significand to the characteristics of the preconditioner blocks. Experiments run on a state-of-the-art GPU accelerator show that our implementation offers attractive runtime savings.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

Compositional Analysis of Organosolv Poplar Lignin by Using High-Performance Liquid Chromatography/High-resolution Multi-stage Tandem Mass Spectrometry

2021 ◽  
Author(s):  
Jifa Zhang ◽  
Yuan Jiang ◽  
Leah F Easterling ◽  
Anton Anster ◽  
Wanru Li ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
High Performance ◽  
Compositional Analysis ◽  
Complex Mixtures ◽  
Tandem Mass ◽  
Multi Stage ◽  
The Individual ◽  
Environmentally Friendly Process

Organosolv treatment is an efficient and environmentally friendly process to degrade lignin into small compounds. The capability of characterizing the individual compounds in the complex mixtures formed upon organosolv treatment...

scholarly journals Combining Heuristics with Simulation and Fuzzy Logic to Solve a Flexible-Size Location Routing Problem under Uncertainty

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 45
Author(s):  
Rafael D. Tordecilla ◽  
Pedro J. Copado-Méndez ◽  
Javier Panadero ◽  
Carlos L. Quintero-Araujo ◽  
Jairo R. Montoya-Torres ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Local Search ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Iterated Local Search ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Fuzzy Pattern ◽  
Reasonable Computing

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.

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