Lenstool-HPC: A High Performance Computing based mass modelling tool for cluster-scale gravitational lenses
arXiv:2004.06352 · doi:10.1016/j.ascom.2019.100360
Abstract
With the upcoming generation of telescopes, cluster scale strong gravitational lenses will act as an increasingly relevant probe of cosmology and dark matter. The better resolved data produced by current and future facilities requires faster and more efficient lens modeling software. Consequently, we present Lenstool-HPC, a strong gravitational lens modeling and map generation tool based on High Performance Computing (HPC) techniques and the renowned Lenstool software. We also showcase the HPC concepts needed for astronomers to increase computation speed through massively parallel execution on supercomputers. Lenstool-HPC was developed using lens modelling algorithms with high amounts of parallelism. Each algorithm was implemented as a highly optimised CPU, GPU and Hybrid CPU-GPU version. The software was deployed and tested on the Piz Daint cluster of the Swiss National Supercomputing Centre (CSCS). Lenstool-HPC perfectly parallel lens map generation and derivative computation achieves a factor 30 speed-up using only 1 GPUs compared to Lenstool. Lenstool-HPC hybrid Lens-model fit generation tested at Hubble Space Telescope precision is scalable up to 200 CPU-GPU nodes and is faster than Lenstool using only 4 CPU-GPU nodes.
9 pages + 4 appendix, published
References in corpus (6)
- A Bayesian approach to strong lensing modelling of galaxy clusters
- Multi-scale cluster lens mass mapping I. Strong Lensing modelling
- New Constraints on the Faint-end of the UV Luminosity Function at z~7-8 using the Gravitational Lensing of the Hubble Frontier Fields Cluster A2744
- Hubble Frontier Fields : A High Precision Strong Lensing Analysis of Galaxy Cluster MACSJ0416.1-2403 using ~200 Multiple Images
- Strong Lensing in Abell 1703: Constraints on the Slope of the Inner Dark Matter Distribution
- The Baryon Fractions and Mass-to-Light Ratios of Early-Type Galaxies