GIGA-Lens 2.0: Strong-Lens Modeling on Multiple GPU Nodes
Xiaosheng Huang, Linus Upson, Nicolas Ratier-Werbin, Harry Lu, Sean Xu, Elden Yap, Evan Odell, Ansel Parke, Harsh Ambardekar, Saul Baltasar, Nestor Demeure, Bradley Richardson, Andi Gu, Yuan-Ming Hsu, Junyi Liu
astro-ph.CO, astro-ph.GA
Submitted: 2026-06-29
Comments: 21 pages, 8 figures, and 3 tables
Code: https://github.com/oogle/jax
License: http://creativecommons.org/licenses/by/4.0/
The gist: We present GIGA-Lens 2.0: a major upgrade to the GPU-accelerated Bayesian framework for modeling strong lensing systems that allows it to be run across multiple GPU nodes.
Terminology
Abstract
We present GIGA-Lens 2.0: a major upgrade to the GPU-accelerated Bayesian framework for modeling strong lensing systems that allows it to be run across multiple GPU nodes. We have succeeded in running GIGA-Lens 2.0 on 128 nodes or 512 A100 GPUs. We demonstrate the speed benefits of this new version, and apply them to modeling 100 simulated systems and a real system, DESI J238.5690+04.7276. We also present other changes to the framework that have yielded further improvement on performance.
Sources
- A Novel Lensed Point Source Modeling Pipeline using GIGA-Lens with Application to SN Zwicky and SN iPTF16geu
- TDCOSMO IV: Hierarchical time-delay cosmography -- joint inference of the Hubble constant and galaxy density profiles
- TensorFlow Distributions
- Demystifying NCCL: An In-depth Analysis of GPU Communication Protocols and Algorithms
- DESI Strong Lens Foundry V: A Sample of HST-Observed Strong Lenses Modeled with GIGA-Lens
- Nested $\hat R$: Assessing the convergence of Markov chain Monte Carlo when running many short chains
- The Carousel Lens I: A Spectroscopic Survey of the Carousel Lens Field
- The Carousel Lens II: Cosmological Constraints with GIGA-Lens
- AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
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