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Improving parallel executions by increasing task granularity in task-based runtime systems using acyclic DAG

Bérenger Bramas^1,2, Alain Ketterlin^1,2,3

¹CAMUS, Inria Nancy - Grand Est, Nancy, France.

Peerj. Computer Science

|April 5, 2021

Summary

This study optimizes task-based parallel applications by aggregating small tasks to reduce overhead. This strategy significantly speeds up execution on multi-core systems.

Keywords:

Clustering DAG Graph Partitioning Task-based

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Area of Science:

Computer Science
Parallel Computing
Algorithm Optimization

Background:

Task-based parallelism represents algorithms as directed acyclic graphs of computational tasks.
Dependency management in task-based execution introduces overhead, particularly with low-cost tasks.
Reducing this overhead is crucial for improving parallel application performance.

Purpose of the Study:

To investigate and enhance task aggregation strategies for parallel execution.
To mitigate the impact of dependency management overhead in task-based applications.
To improve the overall makespan and efficiency of parallel computations.

Main Methods:

Studied an existing task clustering/partitioning strategy.
Developed two novel heuristics to augment the existing strategy.
Introduced a new model for estimating execution duration to guide granularity selection.
Conducted in-depth analysis on a large dataset of task graphs.

Main Results:

The enhanced strategy effectively reduces execution overhead.
The proposed model accurately estimates task execution duration.
Achieved a significant speedup of 7x for a real numerical application on a multi-core system.
Demonstrated the effectiveness of task aggregation for performance improvement.

Conclusions:

Task aggregation is a viable method to accelerate parallel applications.
The developed heuristics and duration estimation model offer practical improvements.
Optimizing task granularity is key to efficient parallel execution in task-based systems.