# Parallelized Inference for Gravitational-Wave Astronomy

## TL;DR

This paper presents a GPU-accelerated approach to Bayesian inference in gravitational-wave astronomy, significantly reducing computation times for waveform generation and population analysis, enabling faster data processing as detector sensitivity and event rates increase.

## Contribution

The authors develop and demonstrate a GPU-based implementation of gravitational-wave inference calculations, achieving substantial speed-ups over CPU methods and making the process more scalable for future data volumes.

## Key findings

- Speed-up of ~50x for waveform generation and likelihood evaluation.
- Over 100x acceleration for population inference.
- Code is publicly available and easy to use without CUDA expertise.

## Abstract

Bayesian inference is the workhorse of gravitational-wave astronomy, for example, determining the mass and spins of merging black holes, revealing the neutron star equation of state, and unveiling the population properties of compact binaries. The science enabled by these inferences comes with a computational cost that can limit the questions we are able to answer. This cost is expected to grow. As detectors improve, the detection rate will go up, allowing less time to analyze each event. Improvement in low-frequency sensitivity will yield longer signals, increasing the number of computations per event. The growing number of entries in the transient catalog will drive up the cost of population studies. While Bayesian inference calculations are not entirely parallelizable, key components are embarrassingly parallel: calculating the gravitational waveform and evaluating the likelihood function. Graphical processor units (GPUs) are adept at such parallel calculations. We report on progress porting gravitational-wave inference calculations to GPUs. Using a single code - which takes advantage of GPU architecture if it is available - we compare computation times using modern GPUs (NVIDIA P100) and CPUs (Intel Gold 6140). We demonstrate speed-ups of $\sim 50 \times$ for compact binary coalescence gravitational waveform generation and likelihood evaluation and more than $100\times$ for population inference within the lifetime of current detectors. Further improvement is likely with continued development. Our python-based code is publicly available and can be used without familiarity with the parallel computing platform, CUDA.