# Accelerating gravitational microlensing simulations using the Xeon Phi coprocessor

## TL;DR

This paper evaluates the performance of the Xeon Phi coprocessor in accelerating gravitational microlensing simulations, comparing it with GPUs and highlighting its potential as a high-performance alternative.

## Contribution

It demonstrates that the Xeon Phi, especially Knights Landing, can significantly accelerate microlensing simulations, offering a promising alternative to GPUs.

## Key findings

- Xeon Phi Knights Corner offers comparable speedup to Fermi GPUs.
- Knights Landing is approximately 5.8 times faster than Knights Corner.
- Knights Landing is about 2.9 times faster than the tested Kepler GPU.

## Abstract

Recently Graphics Processing Units (GPUs) have been used to speed up very CPU-intensive gravitational microlensing simulations. In this work, we use the Xeon Phi coprocessor to accelerate such simulations and compare its performance on a microlensing code with that of NVIDIA's GPUs. For the selected set of parameters evaluated in our experiment, we find that the speedup by Intel's Knights Corner coprocessor is comparable to that by NVIDIA's Fermi family of GPUs with compute capability 2.0, but less significant than GPUs with higher compute capabilities such as the Kepler. However, the very recently released second generation Xeon Phi, Knights Landing, is about 5.8 times faster than the Knights Corner, and about 2.9 times faster than the Kepler GPU used in our simulations. We conclude that the Xeon Phi is a very promising alternative to GPUs for modern high performance microlensing simulations.