Accelerating Dedispersion using Many-Core Architectures
November 09, 2023 Β· Declared Dead Β· π Astrophysical Journal Supplement Series
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jan NovotnΓ½, Karel AdΓ‘mek, M. A. Clark, Mike Giles, Wesley Armour
arXiv ID
2311.05341
Category
astro-ph.IM
Cross-listed
cs.DC
Citations
3
Venue
Astrophysical Journal Supplement Series
Last Checked
1 month ago
Abstract
Astrophysical radio signals are excellent probes of extreme physical processes that emit them. However, to reach Earth, electromagnetic radiation passes through the ionised interstellar medium (ISM), introducing a frequency-dependent time delay (dispersion) to the emitted signal. Removing dispersion enables searches for transient signals like Fast Radio Bursts (FRB) or repeating signals from isolated pulsars or those in orbit around other compact objects. The sheer volume and high resolution of data that next generation radio telescopes will produce require High-Performance Computing (HPC) solutions and algorithms to be used in time-domain data processing pipelines to extract scientifically valuable results in real-time. This paper presents a state-of-the-art implementation of brute force incoherent dedispersion on NVIDIA GPUs, and on Intel and AMD CPUs. We show that our implementation is 4x faster (8-bit 8192 channels input) than other available solutions and demonstrate, using 11 existing telescopes, that our implementation is at least 20 faster than real-time. This work is part of the AstroAccelerate package.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β astro-ph.IM
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
π
π
Old Age
Star-galaxy Classification Using Deep Convolutional Neural Networks
R.I.P.
π»
Ghosted
CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
R.I.P.
π»
Ghosted
Non-negative Matrix Factorization: Robust Extraction of Extended Structures
R.I.P.
π
404 Not Found
Deep Recurrent Neural Networks for Supernovae Classification
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted