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Investigation of Early Plasma Evolution Induced by Ultrashort Laser Pulses

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Robust regression with CUDA and its application to plasma reflectometry.

Diogo R Ferreira¹, Pedro J Carvalho², Horácio Fernandes²

¹Instituto Superior Técnico (IST), Universidade de Lisboa, Campus do Taguspark, Avenida Prof. Dr. Cavaco Silva, 2744-016 Porto Salvo, Portugal.

The Review of Scientific Instruments

|December 3, 2015

Summary

This summary is machine-generated.

This study accelerates robust regression for scientific data with outliers using graphics processing unit (GPU) computing. The parallelized least median of squares method achieves real-time performance for sensitive applications.

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Area of Science:

Data analysis
Computational science
Scientific instrumentation

Background:

Linear regression is crucial for scientific data but sensitive to outliers.
Robust regression methods exist but are computationally intensive for real-time use.

Purpose of the Study:

To adapt robust regression for time-sensitive applications using GPU computing.
To demonstrate performance gains by parallelizing the least median of squares method.

Main Methods:

Graphics processing unit (GPU)-based parallelization of the least median of squares algorithm.
Implementation for analyzing data from plasma diagnostic systems.

Main Results:

Significant acceleration of the least median of squares method using GPU computing.
Achieved real-time usability for robust regression despite high computational complexity.
Demonstrated performance gains applicable to scientific instrumentation data.

Conclusions:

GPU computing enables efficient robust regression for data with outliers.
The parallelized approach is suitable for time-sensitive applications like plasma diagnostics.
This method is transferable to various scientific fields requiring robust data analysis.