MAPPRAISER: A massively parallel map-making framework for multi-kilo pixel CMB experiments Article Swipe
H. El Bouhargani
,
Aygul Jamal
,
D. Beck
,
Josquin Errard
,
Laura Grigori
,
R. Stompor
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.03370
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.03370
Forthcoming cosmic microwave background (CMB) polarized anisotropy experiments have the potential to revolutionize our understanding of the Universe and fundamental physics. The sought-after, tale-telling signatures will be however distributed over voluminous data sets which these experiments will collect. These data sets will need to be efficiently processed and unwanted contributions due to astrophysical, environmental, and instrumental effects characterized and efficiently mitigated in order to uncover the signatures. This poses a significant challenge to data analysis methods, techniques, and software tools which will not only have to be able to cope with huge volumes of data but to do so with unprecedented precision driven by the demanding science goals posed for the new experiments. A keystone of efficient CMB data analysis are solvers of very large linear systems of equations. Such systems appear in very diverse contexts throughout CMB data analysis pipelines, however they typically display similar algebraic structures and can therefore be solved using similar numerical techniques. Linear systems arising in the so-called map-making problem are one of the most prominent and common ones. In this work we present a massively parallel, flexible and extensible framework, comprised of a numerical library, MIDAPACK, and a high level code, MAPPRAISER, which provide tools for solving efficiently such systems. The framework implements iterative solvers based on conjugate gradient techniques: enlarged and preconditioned using different preconditioners. We demonstrate the framework on simulated examples reflecting basic characteristics of the forthcoming data sets issued by ground-based and satellite-borne instruments, executing it on as many as 16,384 compute cores. The software is developed as an open source project freely available to the community at: https://github.com/B3Dcmb/midapack .
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https://openalex.org/W4223622085Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2112.03370Digital Object Identifier
- Title
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MAPPRAISER: A massively parallel map-making framework for multi-kilo pixel CMB experimentsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
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2021-12-06Full publication date if available
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H. El Bouhargani, Aygul Jamal, D. Beck, Josquin Errard, Laura Grigori, R. StomporList of authors in order
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https://escholarship.org/uc/item/2xn740v4Publisher landing page
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.48550/arxiv.2112.03370Direct OA link when available
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Massively parallel, Cosmic microwave background, Pixel, Computer science, Physics, Artificial intelligence, Parallel computing, Optics, AnisotropyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.based | 211 |
| abstract_inverted_index.basic | 230 |
| abstract_inverted_index.code, | 196 |
| abstract_inverted_index.goals | 107 |
| abstract_inverted_index.large | 124 |
| abstract_inverted_index.level | 195 |
| abstract_inverted_index.ones. | 173 |
| abstract_inverted_index.order | 62 |
| abstract_inverted_index.posed | 108 |
| abstract_inverted_index.poses | 68 |
| abstract_inverted_index.these | 34 |
| abstract_inverted_index.tools | 79, 200 |
| abstract_inverted_index.using | 153, 219 |
| abstract_inverted_index.which | 33, 80, 198 |
| abstract_inverted_index.16,384 | 249 |
| abstract_inverted_index.Linear | 157 |
| abstract_inverted_index.appear | 131 |
| abstract_inverted_index.common | 172 |
| abstract_inverted_index.cores. | 251 |
| abstract_inverted_index.cosmic | 1 |
| abstract_inverted_index.driven | 102 |
| abstract_inverted_index.freely | 261 |
| abstract_inverted_index.issued | 237 |
| abstract_inverted_index.linear | 125 |
| abstract_inverted_index.solved | 152 |
| abstract_inverted_index.source | 259 |
| abstract_inverted_index.arising | 159 |
| abstract_inverted_index.compute | 250 |
| abstract_inverted_index.display | 144 |
| abstract_inverted_index.diverse | 134 |
| abstract_inverted_index.effects | 56 |
| abstract_inverted_index.however | 27, 141 |
| abstract_inverted_index.present | 178 |
| abstract_inverted_index.problem | 164 |
| abstract_inverted_index.project | 260 |
| abstract_inverted_index.provide | 199 |
| abstract_inverted_index.science | 106 |
| abstract_inverted_index.similar | 145, 154 |
| abstract_inverted_index.solvers | 121, 210 |
| abstract_inverted_index.solving | 202 |
| abstract_inverted_index.systems | 126, 130, 158 |
| abstract_inverted_index.uncover | 64 |
| abstract_inverted_index.volumes | 92 |
| abstract_inverted_index.Universe | 17 |
| abstract_inverted_index.analysis | 74, 119, 139 |
| abstract_inverted_index.collect. | 37 |
| abstract_inverted_index.contexts | 135 |
| abstract_inverted_index.enlarged | 216 |
| abstract_inverted_index.examples | 228 |
| abstract_inverted_index.flexible | 182 |
| abstract_inverted_index.gradient | 214 |
| abstract_inverted_index.keystone | 114 |
| abstract_inverted_index.library, | 190 |
| abstract_inverted_index.methods, | 75 |
| abstract_inverted_index.physics. | 20 |
| abstract_inverted_index.software | 78, 253 |
| abstract_inverted_index.systems. | 205 |
| abstract_inverted_index.unwanted | 48 |
| abstract_inverted_index.MIDAPACK, | 191 |
| abstract_inverted_index.algebraic | 146 |
| abstract_inverted_index.available | 262 |
| abstract_inverted_index.challenge | 71 |
| abstract_inverted_index.community | 265 |
| abstract_inverted_index.comprised | 186 |
| abstract_inverted_index.conjugate | 213 |
| abstract_inverted_index.demanding | 105 |
| abstract_inverted_index.developed | 255 |
| abstract_inverted_index.different | 220 |
| abstract_inverted_index.efficient | 116 |
| abstract_inverted_index.executing | 243 |
| abstract_inverted_index.framework | 207, 225 |
| abstract_inverted_index.iterative | 209 |
| abstract_inverted_index.massively | 180 |
| abstract_inverted_index.microwave | 2 |
| abstract_inverted_index.mitigated | 60 |
| abstract_inverted_index.numerical | 155, 189 |
| abstract_inverted_index.parallel, | 181 |
| abstract_inverted_index.polarized | 5 |
| abstract_inverted_index.potential | 10 |
| abstract_inverted_index.precision | 101 |
| abstract_inverted_index.processed | 46 |
| abstract_inverted_index.prominent | 170 |
| abstract_inverted_index.simulated | 227 |
| abstract_inverted_index.so-called | 162 |
| abstract_inverted_index.therefore | 150 |
| abstract_inverted_index.typically | 143 |
| abstract_inverted_index.anisotropy | 6 |
| abstract_inverted_index.background | 3 |
| abstract_inverted_index.equations. | 128 |
| abstract_inverted_index.extensible | 184 |
| abstract_inverted_index.framework, | 185 |
| abstract_inverted_index.implements | 208 |
| abstract_inverted_index.map-making | 163 |
| abstract_inverted_index.pipelines, | 140 |
| abstract_inverted_index.reflecting | 229 |
| abstract_inverted_index.signatures | 24 |
| abstract_inverted_index.structures | 147 |
| abstract_inverted_index.throughout | 136 |
| abstract_inverted_index.voluminous | 30 |
| abstract_inverted_index.Forthcoming | 0 |
| abstract_inverted_index.MAPPRAISER, | 197 |
| abstract_inverted_index.demonstrate | 223 |
| abstract_inverted_index.distributed | 28 |
| abstract_inverted_index.efficiently | 45, 59, 203 |
| abstract_inverted_index.experiments | 7, 35 |
| abstract_inverted_index.forthcoming | 234 |
| abstract_inverted_index.fundamental | 19 |
| abstract_inverted_index.signatures. | 66 |
| abstract_inverted_index.significant | 70 |
| abstract_inverted_index.techniques, | 76 |
| abstract_inverted_index.techniques. | 156 |
| abstract_inverted_index.techniques: | 215 |
| abstract_inverted_index.experiments. | 112 |
| abstract_inverted_index.ground-based | 239 |
| abstract_inverted_index.instrumental | 55 |
| abstract_inverted_index.instruments, | 242 |
| abstract_inverted_index.tale-telling | 23 |
| abstract_inverted_index.characterized | 57 |
| abstract_inverted_index.contributions | 49 |
| abstract_inverted_index.revolutionize | 12 |
| abstract_inverted_index.sought-after, | 22 |
| abstract_inverted_index.understanding | 14 |
| abstract_inverted_index.unprecedented | 100 |
| abstract_inverted_index.astrophysical, | 52 |
| abstract_inverted_index.environmental, | 53 |
| abstract_inverted_index.preconditioned | 218 |
| abstract_inverted_index.characteristics | 231 |
| abstract_inverted_index.satellite-borne | 241 |
| abstract_inverted_index.preconditioners. | 221 |
| abstract_inverted_index.https://github.com/B3Dcmb/midapack | 267 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.4233128 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |