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Related Experiment Video

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Tuning a Parallel Segmented Flow Column and Enabling Multiplexed Detection

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Autotuning of Exascale Applications With Anomalies Detection.

Dragi Kimovski¹, Roland Mathá¹, Gabriel Iuhasz^2,3

¹Institute for Information Technology, University of Klagenfurt, Klagenfurt, Austria.

Frontiers in Big Data

|December 13, 2021

Summary

Autotuning exascale applications is complex. This study introduces a novel genetic multi-objective optimization approach within the ASPIDE framework to efficiently identify optimal code and parameters, considering execution time and energy.

Keywords:

IoT applications autotuning events and anomalies detection exascale computing multi-objective optimization

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Area of Science:

High-performance computing
Computational science
Software engineering

Background:

Exascale systems require efficient execution of complex distributed applications.
Autotuning automates the selection of optimal code variations and runtime parameters.
Current autotuning methods struggle with the scale and complexity of exascale environments.

Purpose of the Study:

To develop a novel autotuning approach for exascale applications.
To address the challenges of multi-dimensional parameter spaces and heterogeneous resources.
To integrate optimization with anomaly detection for robust performance.

Main Methods:

A genetic multi-objective optimization algorithm is employed.
The algorithm is integrated into the ASPIDE exascale computing framework.
Pluggable objective functions (e.g., execution time, energy) and machine learning-based event detection are utilized.

Main Results:

The proposed approach handles multi-dimensional search spaces effectively.
It optimizes for multiple objectives, including performance and energy efficiency.
Machine learning detects runtime events and anomalies, enhancing robustness.

Conclusions:

The novel autotuning approach significantly improves the efficiency of exascale application optimization.
Integration within the ASPIDE framework provides a scalable solution.
The method offers a robust strategy for managing complexity and ensuring reliable execution.