A 400Gbit Ethernet core enabling High Data Rate Streaming from FPGAs to Servers and GPUs in Radio Astronomy
November 23, 2024 ยท Declared Dead ยท ๐ United States National Committee of URSI National Radio Science Meeting
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Wei Liu, Mitchell C. Burnett, Dan Werthimer, Jonathon Kocz
arXiv ID
2411.15630
Category
astro-ph.IM
Cross-listed
cs.DC
Citations
1
Venue
United States National Committee of URSI National Radio Science Meeting
Last Checked
1 month ago
Abstract
The increased bandwidth coupled with the large numbers of antennas of several new radio telescope arrays has resulted in an exponential increase in the amount of data that needs to be recorded and processed. In many cases, it is necessary to process this data in real time, as the raw data volumes are too high to be recorded and stored. Due to the ability of graphics processing units (GPUs) to process data in parallel, GPUs are increasingly used for data-intensive tasks. In most radio astronomy digital instrumentation (e.g. correlators for spectral imaging, beamforming, pulsar, fast radio burst and SETI searching), the processing power of modern GPUs is limited by the input/output data rate, not by the GPU's computation ability. Techniques for streaming ultra-high-rate data to GPUs, such as those described in this paper, reduce the number of GPUs and servers needed, and make significant reductions in the cost, power consumption, size, and complexity of GPU based radio astronomy backends. In this research, we developed and tested several different techniques to stream data from network interface cards (NICs) to GPUs. We also developed an open-source UDP/IPv4 400GbE wrapper for the AMD/Xilinx IP demonstrating high-speed data stream transfer from a field programmable gate array (FPGA) to GPU.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ astro-ph.IM
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
๐
๐
Old Age
Star-galaxy Classification Using Deep Convolutional Neural Networks
R.I.P.
๐ป
Ghosted
CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
R.I.P.
๐ป
Ghosted
Non-negative Matrix Factorization: Robust Extraction of Extended Structures
R.I.P.
๐
404 Not Found
Deep Recurrent Neural Networks for Supernovae Classification
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted