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Summary
This summary is machine-generated.

This study introduces an automated scheduler for high-performance computing (HPC) using heterogeneous hardware. The new heuristic approach optimizes task execution, often matching or exceeding expert-defined priorities for better performance.

Keywords:
Heterogeneous schedulingHeteroprioHigh performance computingMulticore architectureParallel computingRuntime systemStarPU

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Area of Science:

  • Computer Science
  • Parallel Computing
  • Algorithm Optimization

Background:

  • High-performance computing (HPC) increasingly utilizes heterogeneous hardware, combining central processing units (CPUs) and graphical processing units (GPUs).
  • Task-based methods offer a promising approach for parallelizing applications on these complex computing architectures.
  • Effective scheduling strategies are crucial for determining task execution order and placement on available processing units.

Purpose of the Study:

  • To develop an automated, heuristic-based scheduling strategy for task-based parallel applications on heterogeneous HPC systems.
  • To configure the Heteroprio scheduler within the StarPU runtime system using automatically generated task priorities.
  • To evaluate the performance of the proposed scheduling approach against expert-defined priorities.

Main Methods:

  • A heuristic approach was developed to assign priorities to task types based on a fitness score for each task/worker combination.
  • These generated priorities were used to automatically configure the Heteroprio scheduler in the StarPU runtime system.
  • Performance was evaluated through both emulated executions and real-world HPC application benchmarks.

Main Results:

  • The automated heuristic-based scheduling approach demonstrated performance equivalent or superior to expert-defined priorities across various HPC applications.
  • The method successfully configured the Heteroprio scheduler, optimizing task distribution on heterogeneous hardware.
  • Emulated and real-case performance evaluations confirmed the effectiveness of the proposed scheduling strategy.

Conclusions:

  • The developed heuristic-based scheduling approach provides an effective and automated method for optimizing task execution in heterogeneous HPC environments.
  • This strategy offers a competitive alternative to manual, expert-driven scheduler configuration, simplifying optimization for complex systems.
  • The findings suggest a pathway for more efficient utilization of resources in modern high-performance computing architectures.