# Accelerating parameter inference with graphics processing units

## TL;DR

This paper demonstrates how adapting parts of the RIFT algorithm to GPUs significantly accelerates gravitational wave parameter inference, reducing computational cost and latency for low-latency multimessenger astronomy.

## Contribution

The paper introduces GPU-accelerated components of the RIFT algorithm, achieving substantial performance improvements in gravitational wave data analysis.

## Key findings

- GPU implementation speeds up inference process
- Reduces overall computational cost
- Enables faster low-latency analysis

## Abstract

Gravitational wave Bayesian parameter inference involves repeated comparisons of GW data to generic candidate predictions. Even with algorithmically efficient methods like RIFT or reduced-order quadrature, the time needed to perform these calculations and overall computational cost can be significant compared to the minutes to hours needed to achieve the goals of low-latency multimessenger astronomy. By translating some elements of the RIFT algorithm to operate on graphics processing units (GPU), we demonstrate substantial performance improvements, enabling dramatically reduced overall cost and latency.