# SimSDP, a Rapid Prototyping tool for Radio Astronomy: From NenuFAR Experiments to SKAO-Scale Simulation

Ophélie Renaud, Nicolas Gac, François Orieux, Cedric Dumez-Viou

► To cite this version:

Ophélie Renaud, Nicolas Gac, François Orieux, Cedric Dumez-Viou. SimSDP, a Rapid Prototyping tool for Radio Astronomy: From NenuFAR Experiments to SKAO-Scale Simulation. SaLF 2025 - 11th Annual conference on Science At Low Frequencies, Dec 2025, Orleans (France), France. pp.1-1. hal-05406207

HAL Id: hal-05406207

https://hal.science/hal-05406207v1

Submitted on 9 Dec 2025

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# DARK ERA

# SimSDP, a Rapid Prototyping tool for Radio Astronomy: From NenuFAR Experiments to SKAO-Scale Simulation

O. Renaud¹², N. Gac¹, F. Orieux³, C. Viou⁴

¹SATIE, Univ. Paris-Saclay, ENS Paris-Saclay, CNRS, Gif-sur-Yvette, France. [surname.name@ens-paris-saclay.fr](mailto:surname.name@ens-paris-saclay.fr)

²IRISA, ENS Rennes, CNRS, France. [first.last@irisa.fr](mailto:first.last@irisa.fr)

³L2S, Univ. Paris-Saclay, CNRS, CentraleSupélec, Gif-sur-Yvette, France. [first.last@l2s.centralesupelec.fr](mailto:first.last@l2s.centralesupelec.fr)

⁴ORN, Observatoire de Paris, Univ. PSL, Univ. Orléans, CNRS, Nançay, France. [first.last@obs-nancay.fr](mailto:first.last@obs-nancay.fr)

This work was supported by DARK-ERA (ANR-20-CE46-0001-01).

## The Square Kilometre Array (SKA) exascale radio telescope

<figure><img src="image_2.png" /><figcaption>Fig. 1: Simplified representation of the SKAO pipeline.</figcaption></figure>

* The **Science Data Processor (SDP)** will process data at terabyte rates with a 1 MW, 250 Pflops budget and use **modern HPC system**.

* Rapid resource estimation is crucial to build HPC system and to manage astronomers' algorithms.

## SimSDP

<figure><img src="image_3.png" /></figure>

* SimSDP, a rapid prototyping tool that simulates radio-imaging pipelines on HPC systems.

* Rely on the Synchronous Dataflow (SDF) graph $G = (A, F)$ with actors A and FIFOs F, capturing task, data, pipeline, and internal parallelism.

## SDP algorithm

<figure><img src="image_4.png" /><figcaption>Fig. 2: Overview of parallelization strategies: (a) independent pipelines per frequency channel; (b) centralized, and (c) decentralized deconvolution across nodes.</figcaption></figure>

* 3 deconvolutions distribution explored

* ✓ Independent (fast, ignores spectral correlations); ✓ Centralized (costly merge, exploits spectral correlations); ✓ Decentralized (tradeoff)

* 3 gridding/degridding techniques explored:

* ✓ DFT: $O(2nv.ng^2)$; ✓ FFT: $O(2ng^2log_2ng^2+nv(nG^2+nD^2))$; ✓ G2G: $O(2ng^2log_2ng^2+nv'(nG^2+nD^2))$

* 3 key parameters explored

* ✓ grid size (ng); ✓ number of visibilities (nv); ✓ number of minor cycle

## Case study: NenuFAR

<table><caption>TABLE I: Summary of NenuFAR MSs used in the real-data experiments.</caption><thead><tr><th>MeasurementSets ⇓</th><th>number of visibilities (nv)</th><th>number of sources (ns)</th><th>frequency channel (nf)</th><th>size</th></tr></thead><tbody><tr><td>SB155</td><td>1.18 × 10<sup>7</sup></td><td>1</td><td>64</td><td>37.2 GB</td></tr><tr><td>SB155.rebin</td><td>1.48 × 10<sup>6</sup></td><td>1</td><td>8</td><td>1.5 GB</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>Fig. 3: Dirty images of the SimSDP prototypes and DDFacet prototypes obtained with the NenuFAR SB155.rebin measurement set.</figcaption></figure>

## Result

<table><caption>TABLE II: Relative error between SimSDP simulations and prototype execution.</caption><thead><tr><th>Deconvolution<br>distribution⇒</th><th>Independent</th><th>Centralized</th></tr></thead><tbody><tr><th>Grid-degrid.<br>techniques ⇓</th><td></td><td></td></tr><tr><th>DFT</th><td>5.55 %</td><td>7.27 %</td></tr><tr><th>FFT</th><td>2.88 %</td><td>6.02 %</td></tr><tr><th>G2G</th><td>6.98 %</td><td>6.22 %</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>Fig. 4: SimSDP simulated latency according to the number of visibilities.</figcaption></figure>

* Simulation error < 10% → Reliable for DSE exploration.

* Near real-time at Pathfinder scale, but not at SKAO scale → need to explore new strategies.

## Summary conclusion

* **Achievement:**

* ✓ Modeling different strategies for spectral parallelism.

* ✓ Simulating pipeline execution on large-scale multi-core, multi-node systems with realistic observation sizes.

* ✓ Prototyping pipelines with real-world data from the NenuFAR instrument.

* **Resource:**

* ✓ SimSDP is implemented into PREESM and available on Github.

* **Future work:**

* ➤ Extend CPU estimation to GPU.

* ➤ Explore decentralized deconvolution distribution.

<figure><img src="image_7.png" /></figure>

[1] N. Monnier et al. Fast grid to grid interpolation for radio interferometric imaging. Astron. Comput., 2023; [2] Nicolas Monnier, et al. Multi-core multi-node parallelization of the radio interferometric imaging pipeline ddfacet. SiPS, 2022; [3] M. Pelcat et al. Preesm: A dataflow-based rapid prototyping framework for simplifying multicore dsp programming. EDERC, 2014; [5] Rau, U. and Cornwell, T. J. A multi-scale multi-frequency deconvolution algorithm for synthesis imaging in radio interferometry. A&A, 2011; [6] O. Smirnov. Revisiting the radio interferometer measurement equation. i. a full-sky jones formalism. Astron. Astro., 2011. [7] S. Wang et al. An initial framework for prototyping radio-interferometric imaging pipelines. DASIP, 2024; [8] S. Wang et al. A Decentralized Framework for Radio-interferometric Image Reconstruction. The Astro. Journal, 2025.