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Related Experiment Video

Updated: May 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Scheduling in heterogeneous computing environments for proximity queries.

Duksu Kim1, Jinkyu Lee, Junghwan Lee

  • 1Department of Computer Science, Korea Advanced Institute of Science and Technology, Room #3440, 291 Daehak-ro (373-1 Guseong-dong), Yuseong-gu, Daejeon, Chungcheongnam-do, 305-701 (KS015), Republic of Korea.

IEEE Transactions on Visualization and Computer Graphics
|July 13, 2013
PubMed
Summary
This summary is machine-generated.

A new linear programming (LP) scheduling algorithm accelerates proximity queries on heterogeneous computing architectures (CPUs and GPUs). This method significantly improves performance, outperforming prior approaches and manual optimizations.

Related Experiment Videos

Last Updated: May 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • Computer Science
  • High-Performance Computing
  • Algorithm Design

Background:

  • Proximity queries are computationally intensive and benefit from parallel processing.
  • Heterogeneous computing architectures (CPUs and GPUs) offer potential for performance gains but pose scheduling challenges.
  • Existing scheduling methods struggle to efficiently utilize diverse computational resources.

Purpose of the Study:

  • To develop a novel scheduling algorithm for heterogeneous multicore architectures (CPUs and GPUs) to accelerate proximity queries.
  • To create an accurate performance model for heterogeneous computations.
  • To formulate and solve an optimization problem for efficient resource allocation.

Main Methods:

  • Proposed a linear programming (LP)-based iterative scheduling algorithm.
  • Developed a model to estimate the running time of proximity query computations on heterogeneous resources.
  • Formulated an optimization problem to minimize the maximum resource-bound execution time.
  • Applied the algorithm to proximity queries across five diverse applications.
  • Integrated the approach with a work-stealing method.

Main Results:

  • Achieved an order of magnitude performance improvement using multiple CPUs and GPUs compared to a single CPU.
  • Demonstrated continuous performance improvement with the addition of more computing resources.
  • Showcased significantly higher performance gains with increased heterogeneity in computing resources.
  • Outperformed a manually optimized parallel method for one application.
  • Achieved results close to the ideal throughput upper bound (75%).
  • Enhanced a work-stealing method, yielding an average 18% performance improvement.

Conclusions:

  • The proposed LP-based scheduling algorithm is efficient and robust for accelerating proximity queries on heterogeneous architectures.
  • The method offers superior performance compared to existing scheduling techniques, especially with increased resource heterogeneity.
  • The algorithm's generality allows for broad applicability across various proximity query types and applications.
  • Integration with work-stealing further enhances performance, indicating wide applicability of the core approach.