Accelerating gravitational microlensing simulations using the Xeon Phi coprocessor

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.

An Improved GPU-based Ray-shooting Code for Gravitational Microlensing

“…Great effort has been expended to deal with this problem in two ways: (i) to perform the computation in parallel and (ii) to reduce the number of lenses (N * ) or light rays (N rays ) involved in the calculations. For the first one, different parallel strategies based on both CPUs (Chen et al 2017) and graphics processing units (GPUs) have been applied to the IRS method, between which GPUs have shown great computational potential (Thompson et al 2010). For the second, Wambsganss (1999) introduced a hierarchical tree method to reorder the lenses by their distance to individual light rays and group distant lenses into larger cells, allowing the N * directly involved in the calculations to be reduced.…”

An Improved GPU-Based Ray-Shooting Code For Gravitational Microlensing

“…(ii) To reduce the number of lenses (N * ) or light rays (N rays ) involved in the calculations. For the first direction, different parallel strategies based on both CPUs Chen et al (2017) and GPUs have been applied on IRS method, among which GPUs have shown great computational potential Thompson et al (2010). For the second direction, Wambsganss (1999) introduced hierarchical tree method to reorganize the lenses by their distance to individual light and group distant lenses into larger cells, allowing N * directly involved in calculations to be reduced.…”

A survey on evaluating and optimizing performance of Intel Xeon Phi

“…,[12][13][14][16][17][18]20,21,24,[27][28][29][30][33][34][35][36][37]39,[42][43][44][45][46][47][48]50,52,55,57,59,63,66,84,86,90,92,93,95,99 IntelMKL 2,17,19,31,32,40,93,99 …”