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DTSTAMP:20260522T162631Z
LOCATION:Bldg. 6 - Room 104
DTSTART;TZID=Europe/Stockholm:20260701T113000
DTEND;TZID=Europe/Stockholm:20260701T120000
UID:submissions.pasc-conference.org_PASC26_sess177_pap104@linklings.com
SUMMARY:astroCAMP: A Co-design Framework  for Sustainable Radio-Interferom
 etric Imaging
DESCRIPTION:Denisa-Andreea Constantinescu (EPFL); Rubén Rodríguez Álvarez 
 (Embedded Systems Laboratory, EPFL); Jacques Morin (Univ Rennes, INSA Renn
 es); Etienne Orliac (SCITAS, EPFL); Mickaël Dardaillon (Univ Rennes, INSA 
 Rennes); Sunrise Wang (Université Côte d'Azur, Côte d'Azur Observatory); H
 ugo Miomandre (Univ Rennes, INSA Rennes); Miguel Peón-Quirós (EcoCloud, EP
 FL); Jean-François Nezan (Univ Rennes, INSA Rennes); and David Atienza (Em
 bedded Systems Laboratory, EPFL)\n\nThe Square Kilometre Array (SKA) will 
 operate one of the world’s largest continuous scientific data systems, sus
 taining petascale imaging under strict power envelopes. Yet current radio-
 interferometric pipelines typically achieve only 4–14\% of hardware peak b
 ecause of memory and I/O bottlenecks, resulting in high energy, operationa
 l, and carbon costs. Progress is further constrained by the absence of sta
 ndardised cross-layer metrics and survey-level fidelity tolerances for pri
 ncipled hardware–software co-design.\nWe present astroCAMP, a reproducible
  benchmarking and co-design framework for SKA-scale imaging. astroCAMP con
 tributes: (1) a unified metric suite spanning performance, utilisation, me
 mory/data-movement behavior, sustainability, economics, and scientific fid
 elity; (2) standardised SKA-representative datasets, reference outputs, an
 d benchmark configurations for reproducible cross-platform evaluation; (3)
  a multi-objective co-design formulation linking quality constraints to ti
 me-, energy-, carbon-, and cost-to-solution; and (4) a reproducible design
 -space exploration workflow to derive Pareto-optimal operating regions.\nW
 e release datasets, scripts, benchmark results, and a reproducibility kit,
  and evaluate WSClean+IDG on an AMD EPYC 9334 CPU and an NVIDIA H100 GPU. 
 The evaluation shows substantial end-to-end orchestration and synchronizat
 ion bottlenecks despite efficient kernels in active phases, limited CPU st
 rong scaling, and location-dependent carbon/cost efficiency under realisti
 c grid and electricity-price assumptions.  We further illustrate the use o
 f astroCAMP for heterogeneous CPU–FPGA design-space exploration, and its p
 otential to facilitate the identification of Pareto-optimal operating poin
 ts for SKA-scale imaging deployments. Lastly, we call on the SKA community
  to define quantifiable fidelity metrics and thresholds to accelerate prin
 cipled optimisation for SKA-scale imaging.\n\nSession Chair: Aldas Lenkšas
  (Politecnico di Milano)\n\n
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