astroCAMP: A Community Benchmark and Co-Design Framework for Sustainable SKA-Scale Radio Imaging
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
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2025
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| _version_ | 1866916008046886912 |
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| author | Constantinescu, Denisa-Andreea Álvarez, Rubén Rodríguez Morin, Jacques Orliac, Etienne Dardaillon, Mickaël Wang, Sunrise Miomandre, Hugo Peón-Quirós, Miguel Nezan, Jean-François Atienza, David |
| author_facet | Constantinescu, Denisa-Andreea Álvarez, Rubén Rodríguez Morin, Jacques Orliac, Etienne Dardaillon, Mickaël Wang, Sunrise Miomandre, Hugo Peón-Quirós, Miguel Nezan, Jean-François Atienza, David |
| contents | The Square Kilometre Array (SKA) will operate one of the world's largest continuous scientific data systems, sustaining petascale imaging under strict power envelopes. Current radio-interferometric pipelines typically achieve only 4-14% of hardware peak utilization due to memory and I/O bottlenecks, incurring high energy, operational, and carbon costs, further compounded by the absence of standardised cross-layer metrics and fidelity tolerances for principled hardware--software co-design.
We present astroCAMP, a reproducible benchmarking and co-design framework for SKA-scale imaging, contributing: (1) a unified metric suite spanning performance, utilisation, memory/data-movement, sustainability, economics, and scientific fidelity; (2) standardised SKA-representative datasets and benchmark configurations for reproducible cross-platform evaluation; (3) a multi-objective co-design formulation linking quality constraints to time-, energy-, carbon-, and cost-to-solution; and (4) a design-space exploration workflow to derive Pareto-optimal operating regions.
We evaluate WSClean+IDG on an AMD EPYC 9334 CPU and NVIDIA H100 GPU, revealing orchestration and synchronization bottlenecks despite efficient kernels, limited CPU strong scaling, and location-dependent carbon/cost efficiency. We illustrate astroCAMP for heterogeneous CPU--FPGA exploration and call on the SKA community to define quantifiable fidelity thresholds to accelerate principled optimisation for SKA-scale imaging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13591 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | astroCAMP: A Community Benchmark and Co-Design Framework for Sustainable SKA-Scale Radio Imaging Constantinescu, Denisa-Andreea Álvarez, Rubén Rodríguez Morin, Jacques Orliac, Etienne Dardaillon, Mickaël Wang, Sunrise Miomandre, Hugo Peón-Quirós, Miguel Nezan, Jean-François Atienza, David Distributed, Parallel, and Cluster Computing Instrumentation and Methods for Astrophysics Performance B.8.2; C.0; C.1.4; C.4; C.5.5; J.4; K.1; K.4.1; K.6.4 The Square Kilometre Array (SKA) will operate one of the world's largest continuous scientific data systems, sustaining petascale imaging under strict power envelopes. Current radio-interferometric pipelines typically achieve only 4-14% of hardware peak utilization due to memory and I/O bottlenecks, incurring high energy, operational, and carbon costs, further compounded by the absence of standardised cross-layer metrics and fidelity tolerances for principled hardware--software co-design. We present astroCAMP, a reproducible benchmarking and co-design framework for SKA-scale imaging, contributing: (1) a unified metric suite spanning performance, utilisation, memory/data-movement, sustainability, economics, and scientific fidelity; (2) standardised SKA-representative datasets and benchmark configurations for reproducible cross-platform evaluation; (3) a multi-objective co-design formulation linking quality constraints to time-, energy-, carbon-, and cost-to-solution; and (4) a design-space exploration workflow to derive Pareto-optimal operating regions. We evaluate WSClean+IDG on an AMD EPYC 9334 CPU and NVIDIA H100 GPU, revealing orchestration and synchronization bottlenecks despite efficient kernels, limited CPU strong scaling, and location-dependent carbon/cost efficiency. We illustrate astroCAMP for heterogeneous CPU--FPGA exploration and call on the SKA community to define quantifiable fidelity thresholds to accelerate principled optimisation for SKA-scale imaging. |
| title | astroCAMP: A Community Benchmark and Co-Design Framework for Sustainable SKA-Scale Radio Imaging |
| topic | Distributed, Parallel, and Cluster Computing Instrumentation and Methods for Astrophysics Performance B.8.2; C.0; C.1.4; C.4; C.5.5; J.4; K.1; K.4.1; K.6.4 |
| url | https://arxiv.org/abs/2512.13591 |