Using GPU Simulation to Accurately Fit to the Power-Law Distribution

TL;DR

This paper presents a GPU-accelerated methodology for fitting data to the power-law distribution, enabling precise goodness-of-fit testing through extensive simulations and detailed cutoff tables.

Contribution

It introduces a GPU-based simulation approach that significantly speeds up the calculation of cutoff values for power-law fit assessment using maximum likelihood estimation.

Findings

60x faster computation with GPU acceleration

Detailed cutoff tables for goodness-of-fit testing

Enhanced accuracy in power-law distribution fitting

Abstract

This article describes a methodology for fitting experimental data to the discrete power-law distribution and provides the results of a detailed simulation exercise used to calculate accurate cutoff values used to assess the fit to a power-law distribution when using the maximum likelihood estimation for the exponent of the distribution. Using massively parallel programming computing, we were able to accelerate by a factor of 60 the computational time required for these calculations across a range of parameters and construct a series of detailed tables containing the test values to be used in a Kolmogorov-Smirnov goodness-of-fit test, allowing for an accurate assessment of the power-law fit from empirical data.