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Optimization of the Ugi Reaction Using Parallel Synthesis and Automated Liquid Handling
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A DAG scheduling scheme on heterogeneous computing systems using tuple-based chemical reaction optimization.

Yuyi Jiang1, Zhiqing Shao1, Yi Guo1

  • 1College of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.

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|August 22, 2014
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Summary
This summary is machine-generated.

This study introduces a novel Tuple Molecular Structure-based Chemical Reaction Optimization (TMSCRO) for efficient directed acyclic graph (DAG) scheduling on heterogeneous computing systems. TMSCRO accelerates convergence and improves scheduling performance, addressing NP-complete optimization problems.

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Area of Science:

  • Computer Science
  • Computational Science
  • Artificial Intelligence

Background:

  • Complex computing problems require efficient solutions on multi-node systems.
  • Task scheduling using directed acyclic graphs (DAGs) is crucial for parallel processing.
  • Finding optimal DAG scheduling is an NP-complete problem, necessitating advanced algorithms.

Purpose of the Study:

  • To propose a novel metaheuristic algorithm, Tuple Molecular Structure-based Chemical Reaction Optimization (TMSCRO), for DAG scheduling.
  • To enhance the efficiency and effectiveness of DAG scheduling on heterogeneous computing systems.
  • To improve upon existing Chemical Reaction Optimization (CRO)-based methods for DAG scheduling.

Main Methods:

  • Developed a TMSCRO algorithm utilizing a tuple molecular structure and four elementary reaction operators.
  • Integrated concepts of constrained critical paths (CCPs), CCP-DAGs, and super molecules for accelerated convergence.
  • Employed metaheuristic optimization inspired by chemical reactions.

Main Results:

  • TMSCRO demonstrates a more reasonable design compared to other CRO-based DAG scheduling algorithms.
  • The algorithm effectively accelerates convergence through CCPs, CCPDAGs, and super molecules.
  • Simulation experiments validated the effectiveness and efficiency of TMSCRO on diverse datasets.

Conclusions:

  • TMSCRO offers a superior approach to DAG scheduling on heterogeneous computing systems.
  • The proposed method provides an efficient solution for NP-complete DAG scheduling problems.
  • TMSCRO represents a significant advancement in metaheuristic optimization for computational tasks.