HyperModels - A Framework for GPU Accelerated Physical Modelling Sound Synthesis Article Swipe
Harri Renney
,
Silvin Willemsen
,
Benedict R. Gaster
,
T. M. Mitchell
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21428/92fbeb44.98a4210a
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21428/92fbeb44.98a4210a
Physical modelling sound synthesis methods generate vast and intricate sound spaces that are navigated using meaningful parameters. Numerical based physical modelling nsynthesis methods provide authentic representations of the physics they model. Unfortunately, the application of these physical models are often limited because of their considerable computational requirements. In previous studies, the CPU has been shown to reliably support two-dimensional linear finite-difference models in real-time with resolutions up to 64x64. However, the near-ubiquitous parallel processing units known as GPUs have previously been used to process considerably larger resolutions, as high as 512×512 in real-time. GPU programming requires a low-level understanding of the architecture, which often imposes a barrier for entry for inexperienced practitioners. Therefore, this paper proposes HyperModels, a framework for automating the mapping of linear finite-difference based physical modelling synthesis into an optimised parallel form suitable for the GPU. An implementation of the design is then used to evaluate the objective performance of the framework by comparing the automated solution to manually developed equivalents. For the majority of the extensive performance profiling tests, the auto-generated programs were observed to perform only 6\% slower but in the worst-case scenario it was 50\% slower. The initial results suggests that, in most circumstances, the automation provided by the framework avoids the low-level expertise required to manually optimise the GPU, with only a small reduction in performance. However, there is still scope to improve the auto-generated optimisations. When comparing the performance of CPU to GPU equivalents, the parallel CPU version supports resolutions of up to 128x128 whilst the GPU continues to support higher resolutions up to 512x512. To conclude the paper, two instruments are developed using HyperModels based on established physical model designs.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.21428/92fbeb44.98a4210a
- https://nime.pubpub.org/pub/ludxkhhz/download/pdf
- OA Status
- bronze
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283023729
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4283023729Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21428/92fbeb44.98a4210aDigital Object Identifier
- Title
-
HyperModels - A Framework for GPU Accelerated Physical Modelling Sound SynthesisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-16Full publication date if available
- Authors
-
Harri Renney, Silvin Willemsen, Benedict R. Gaster, T. M. MitchellList of authors in order
- Landing page
-
https://doi.org/10.21428/92fbeb44.98a4210aPublisher landing page
- PDF URL
-
https://nime.pubpub.org/pub/ludxkhhz/download/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://nime.pubpub.org/pub/ludxkhhz/download/pdfDirect OA link when available
- Concepts
-
Computer science, General-purpose computing on graphics processing units, Sound (geography), Computer graphics (images), Parallel computing, Computational science, Acoustics, Graphics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4283023729 |
|---|---|
| doi | https://doi.org/10.21428/92fbeb44.98a4210a |
| ids.doi | https://doi.org/10.21428/92fbeb44.98a4210a |
| ids.openalex | https://openalex.org/W4283023729 |
| fwci | 0.0 |
| type | article |
| title | HyperModels - A Framework for GPU Accelerated Physical Modelling Sound Synthesis |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11349 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9997000098228455 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Music Technology and Sound Studies |
| topics[1].id | https://openalex.org/T11309 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9970999956130981 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1711 |
| topics[1].subfield.display_name | Signal Processing |
| topics[1].display_name | Music and Audio Processing |
| topics[2].id | https://openalex.org/T10904 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9729999899864197 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1708 |
| topics[2].subfield.display_name | Hardware and Architecture |
| topics[2].display_name | Embedded Systems Design Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7417646050453186 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C50630238 |
| concepts[1].level | 3 |
| concepts[1].score | 0.460495263338089 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q971505 |
| concepts[1].display_name | General-purpose computing on graphics processing units |
| concepts[2].id | https://openalex.org/C203718221 |
| concepts[2].level | 2 |
| concepts[2].score | 0.42448630928993225 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q491713 |
| concepts[2].display_name | Sound (geography) |
| concepts[3].id | https://openalex.org/C121684516 |
| concepts[3].level | 1 |
| concepts[3].score | 0.38837605714797974 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7600677 |
| concepts[3].display_name | Computer graphics (images) |
| concepts[4].id | https://openalex.org/C173608175 |
| concepts[4].level | 1 |
| concepts[4].score | 0.365143358707428 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q232661 |
| concepts[4].display_name | Parallel computing |
| concepts[5].id | https://openalex.org/C459310 |
| concepts[5].level | 1 |
| concepts[5].score | 0.33368849754333496 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q117801 |
| concepts[5].display_name | Computational science |
| concepts[6].id | https://openalex.org/C24890656 |
| concepts[6].level | 1 |
| concepts[6].score | 0.14495772123336792 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q82811 |
| concepts[6].display_name | Acoustics |
| concepts[7].id | https://openalex.org/C21442007 |
| concepts[7].level | 2 |
| concepts[7].score | 0.09418472647666931 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1027879 |
| concepts[7].display_name | Graphics |
| concepts[8].id | https://openalex.org/C121332964 |
| concepts[8].level | 0 |
| concepts[8].score | 0.08500981330871582 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[8].display_name | Physics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7417646050453186 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/general-purpose-computing-on-graphics-processing-units |
| keywords[1].score | 0.460495263338089 |
| keywords[1].display_name | General-purpose computing on graphics processing units |
| keywords[2].id | https://openalex.org/keywords/sound |
| keywords[2].score | 0.42448630928993225 |
| keywords[2].display_name | Sound (geography) |
| keywords[3].id | https://openalex.org/keywords/computer-graphics |
| keywords[3].score | 0.38837605714797974 |
| keywords[3].display_name | Computer graphics (images) |
| keywords[4].id | https://openalex.org/keywords/parallel-computing |
| keywords[4].score | 0.365143358707428 |
| keywords[4].display_name | Parallel computing |
| keywords[5].id | https://openalex.org/keywords/computational-science |
| keywords[5].score | 0.33368849754333496 |
| keywords[5].display_name | Computational science |
| keywords[6].id | https://openalex.org/keywords/acoustics |
| keywords[6].score | 0.14495772123336792 |
| keywords[6].display_name | Acoustics |
| keywords[7].id | https://openalex.org/keywords/graphics |
| keywords[7].score | 0.09418472647666931 |
| keywords[7].display_name | Graphics |
| keywords[8].id | https://openalex.org/keywords/physics |
| keywords[8].score | 0.08500981330871582 |
| keywords[8].display_name | Physics |
| language | en |
| locations[0].id | doi:10.21428/92fbeb44.98a4210a |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4363606845 |
| locations[0].source.issn | |
| locations[0].source.type | conference |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | NIME 2022 |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | |
| locations[0].pdf_url | https://nime.pubpub.org/pub/ludxkhhz/download/pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | NIME 2022 |
| locations[0].landing_page_url | https://doi.org/10.21428/92fbeb44.98a4210a |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5009398698 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1481-5852 |
| authorships[0].author.display_name | Harri Renney |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I178535277 |
| authorships[0].affiliations[0].raw_affiliation_string | University of the West of England |
| authorships[0].institutions[0].id | https://openalex.org/I178535277 |
| authorships[0].institutions[0].ror | https://ror.org/02nwg5t34 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I178535277 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | University of the West of England |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Harri Renney |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | University of the West of England |
| authorships[1].author.id | https://openalex.org/A5023074394 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4062-5473 |
| authorships[1].author.display_name | Silvin Willemsen |
| authorships[1].countries | DK |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I891191580 |
| authorships[1].affiliations[0].raw_affiliation_string | Aalborg University Copenhagen |
| authorships[1].institutions[0].id | https://openalex.org/I891191580 |
| authorships[1].institutions[0].ror | https://ror.org/04m5j1k67 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I891191580 |
| authorships[1].institutions[0].country_code | DK |
| authorships[1].institutions[0].display_name | Aalborg University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Silvin Willemsen |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Aalborg University Copenhagen |
| authorships[2].author.id | https://openalex.org/A5047465290 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Benedict R. Gaster |
| authorships[2].countries | GB |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I178535277 |
| authorships[2].affiliations[0].raw_affiliation_string | University of the West of England |
| authorships[2].institutions[0].id | https://openalex.org/I178535277 |
| authorships[2].institutions[0].ror | https://ror.org/02nwg5t34 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I178535277 |
| authorships[2].institutions[0].country_code | GB |
| authorships[2].institutions[0].display_name | University of the West of England |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Benedict Gaster |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | University of the West of England |
| authorships[3].author.id | https://openalex.org/A5012149761 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-0809-1528 |
| authorships[3].author.display_name | T. M. Mitchell |
| authorships[3].countries | GB |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I178535277 |
| authorships[3].affiliations[0].raw_affiliation_string | University of the West of England |
| authorships[3].institutions[0].id | https://openalex.org/I178535277 |
| authorships[3].institutions[0].ror | https://ror.org/02nwg5t34 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I178535277 |
| authorships[3].institutions[0].country_code | GB |
| authorships[3].institutions[0].display_name | University of the West of England |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Tom Mitchell |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | University of the West of England |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://nime.pubpub.org/pub/ludxkhhz/download/pdf |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | HyperModels - A Framework for GPU Accelerated Physical Modelling Sound Synthesis |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11349 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9997000098228455 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Music Technology and Sound Studies |
| related_works | https://openalex.org/W2505380084, https://openalex.org/W4400333498, https://openalex.org/W2086739451, https://openalex.org/W2909726438, https://openalex.org/W1980160788, https://openalex.org/W1656096860, https://openalex.org/W2095928260, https://openalex.org/W2268149564, https://openalex.org/W1984739956, https://openalex.org/W2763312740 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.21428/92fbeb44.98a4210a |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4363606845 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | conference |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | NIME 2022 |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://nime.pubpub.org/pub/ludxkhhz/download/pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | proceedings-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | NIME 2022 |
| best_oa_location.landing_page_url | https://doi.org/10.21428/92fbeb44.98a4210a |
| primary_location.id | doi:10.21428/92fbeb44.98a4210a |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4363606845 |
| primary_location.source.issn | |
| primary_location.source.type | conference |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | NIME 2022 |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | |
| primary_location.pdf_url | https://nime.pubpub.org/pub/ludxkhhz/download/pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | NIME 2022 |
| primary_location.landing_page_url | https://doi.org/10.21428/92fbeb44.98a4210a |
| publication_date | 2022-06-16 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W1560393114, https://openalex.org/W6740493736, https://openalex.org/W6634910898, https://openalex.org/W6651571937, https://openalex.org/W2789217093, https://openalex.org/W3040216631, https://openalex.org/W6630196586, https://openalex.org/W2593610709, https://openalex.org/W6632883838, https://openalex.org/W1482796577, https://openalex.org/W3195458468, https://openalex.org/W3168059133, https://openalex.org/W2979820863, https://openalex.org/W3201417165, https://openalex.org/W2946862949, https://openalex.org/W2791447271, https://openalex.org/W1994061133, https://openalex.org/W1584690847, https://openalex.org/W7032930454, https://openalex.org/W2315896104, https://openalex.org/W2978223260, https://openalex.org/W1551466219, https://openalex.org/W1997221482, https://openalex.org/W3139348098, https://openalex.org/W6991625617, https://openalex.org/W6650725578, https://openalex.org/W2083367980, https://openalex.org/W2954229653, https://openalex.org/W2121209978, https://openalex.org/W6715759600, https://openalex.org/W210673478, https://openalex.org/W3189586369, https://openalex.org/W3015151796, https://openalex.org/W3167186309, https://openalex.org/W1585901050, https://openalex.org/W2499908148, https://openalex.org/W3208172684, https://openalex.org/W1551385220, https://openalex.org/W2729534234, https://openalex.org/W1504021810, https://openalex.org/W4300831640, https://openalex.org/W2780168842, https://openalex.org/W4292069163, https://openalex.org/W4249595233 |
| referenced_works_count | 44 |
| abstract_inverted_index.a | 96, 105, 117, 218 |
| abstract_inverted_index.An | 139 |
| abstract_inverted_index.In | 47 |
| abstract_inverted_index.To | 263 |
| abstract_inverted_index.an | 131 |
| abstract_inverted_index.as | 76, 87, 89 |
| abstract_inverted_index.by | 155, 203 |
| abstract_inverted_index.in | 62, 91, 184, 197, 221 |
| abstract_inverted_index.is | 144, 225 |
| abstract_inverted_index.it | 188 |
| abstract_inverted_index.of | 26, 34, 42, 99, 123, 141, 152, 167, 237, 248 |
| abstract_inverted_index.on | 274 |
| abstract_inverted_index.to | 55, 67, 82, 147, 160, 178, 211, 228, 239, 250, 256, 261 |
| abstract_inverted_index.up | 66, 249, 260 |
| abstract_inverted_index.6\% | 181 |
| abstract_inverted_index.CPU | 51, 238, 244 |
| abstract_inverted_index.For | 164 |
| abstract_inverted_index.GPU | 93, 240, 254 |
| abstract_inverted_index.The | 192 |
| abstract_inverted_index.and | 7 |
| abstract_inverted_index.are | 12, 38, 269 |
| abstract_inverted_index.but | 183 |
| abstract_inverted_index.for | 107, 109, 119, 136 |
| abstract_inverted_index.has | 52 |
| abstract_inverted_index.the | 27, 32, 50, 70, 100, 121, 137, 142, 149, 153, 157, 165, 168, 173, 185, 200, 204, 207, 214, 230, 235, 242, 253, 265 |
| abstract_inverted_index.two | 267 |
| abstract_inverted_index.was | 189 |
| abstract_inverted_index.50\% | 190 |
| abstract_inverted_index.GPU, | 215 |
| abstract_inverted_index.GPU. | 138 |
| abstract_inverted_index.GPUs | 77 |
| abstract_inverted_index.When | 233 |
| abstract_inverted_index.been | 53, 80 |
| abstract_inverted_index.form | 134 |
| abstract_inverted_index.have | 78 |
| abstract_inverted_index.high | 88 |
| abstract_inverted_index.into | 130 |
| abstract_inverted_index.most | 198 |
| abstract_inverted_index.only | 180, 217 |
| abstract_inverted_index.that | 11 |
| abstract_inverted_index.then | 145 |
| abstract_inverted_index.they | 29 |
| abstract_inverted_index.this | 113 |
| abstract_inverted_index.used | 81, 146 |
| abstract_inverted_index.vast | 6 |
| abstract_inverted_index.were | 176 |
| abstract_inverted_index.with | 64, 216 |
| abstract_inverted_index.based | 18, 126, 273 |
| abstract_inverted_index.entry | 108 |
| abstract_inverted_index.known | 75 |
| abstract_inverted_index.model | 277 |
| abstract_inverted_index.often | 39, 103 |
| abstract_inverted_index.paper | 114 |
| abstract_inverted_index.scope | 227 |
| abstract_inverted_index.shown | 54 |
| abstract_inverted_index.small | 219 |
| abstract_inverted_index.sound | 2, 9 |
| abstract_inverted_index.still | 226 |
| abstract_inverted_index.that, | 196 |
| abstract_inverted_index.their | 43 |
| abstract_inverted_index.there | 224 |
| abstract_inverted_index.these | 35 |
| abstract_inverted_index.units | 74 |
| abstract_inverted_index.using | 14, 271 |
| abstract_inverted_index.which | 102 |
| abstract_inverted_index.64x64. | 68 |
| abstract_inverted_index.avoids | 206 |
| abstract_inverted_index.design | 143 |
| abstract_inverted_index.higher | 258 |
| abstract_inverted_index.larger | 85 |
| abstract_inverted_index.linear | 59, 124 |
| abstract_inverted_index.model. | 30 |
| abstract_inverted_index.models | 37, 61 |
| abstract_inverted_index.paper, | 266 |
| abstract_inverted_index.slower | 182 |
| abstract_inverted_index.spaces | 10 |
| abstract_inverted_index.tests, | 172 |
| abstract_inverted_index.whilst | 252 |
| abstract_inverted_index.128x128 | 251 |
| abstract_inverted_index.barrier | 106 |
| abstract_inverted_index.because | 41 |
| abstract_inverted_index.imposes | 104 |
| abstract_inverted_index.improve | 229 |
| abstract_inverted_index.initial | 193 |
| abstract_inverted_index.limited | 40 |
| abstract_inverted_index.mapping | 122 |
| abstract_inverted_index.methods | 4, 22 |
| abstract_inverted_index.perform | 179 |
| abstract_inverted_index.physics | 28 |
| abstract_inverted_index.process | 83 |
| abstract_inverted_index.provide | 23 |
| abstract_inverted_index.results | 194 |
| abstract_inverted_index.slower. | 191 |
| abstract_inverted_index.support | 57, 257 |
| abstract_inverted_index.version | 245 |
| abstract_inverted_index.512x512. | 262 |
| abstract_inverted_index.512×512 | 90 |
| abstract_inverted_index.However, | 69, 223 |
| abstract_inverted_index.Physical | 0 |
| abstract_inverted_index.conclude | 264 |
| abstract_inverted_index.designs. | 278 |
| abstract_inverted_index.evaluate | 148 |
| abstract_inverted_index.generate | 5 |
| abstract_inverted_index.majority | 166 |
| abstract_inverted_index.manually | 161, 212 |
| abstract_inverted_index.observed | 177 |
| abstract_inverted_index.optimise | 213 |
| abstract_inverted_index.parallel | 72, 133, 243 |
| abstract_inverted_index.physical | 19, 36, 127, 276 |
| abstract_inverted_index.previous | 48 |
| abstract_inverted_index.programs | 175 |
| abstract_inverted_index.proposes | 115 |
| abstract_inverted_index.provided | 202 |
| abstract_inverted_index.reliably | 56 |
| abstract_inverted_index.required | 210 |
| abstract_inverted_index.requires | 95 |
| abstract_inverted_index.scenario | 187 |
| abstract_inverted_index.solution | 159 |
| abstract_inverted_index.studies, | 49 |
| abstract_inverted_index.suggests | 195 |
| abstract_inverted_index.suitable | 135 |
| abstract_inverted_index.supports | 246 |
| abstract_inverted_index.Numerical | 17 |
| abstract_inverted_index.authentic | 24 |
| abstract_inverted_index.automated | 158 |
| abstract_inverted_index.comparing | 156, 234 |
| abstract_inverted_index.continues | 255 |
| abstract_inverted_index.developed | 162, 270 |
| abstract_inverted_index.expertise | 209 |
| abstract_inverted_index.extensive | 169 |
| abstract_inverted_index.framework | 118, 154, 205 |
| abstract_inverted_index.intricate | 8 |
| abstract_inverted_index.low-level | 97, 208 |
| abstract_inverted_index.modelling | 1, 20, 128 |
| abstract_inverted_index.navigated | 13 |
| abstract_inverted_index.objective | 150 |
| abstract_inverted_index.optimised | 132 |
| abstract_inverted_index.profiling | 171 |
| abstract_inverted_index.real-time | 63 |
| abstract_inverted_index.reduction | 220 |
| abstract_inverted_index.synthesis | 3, 129 |
| abstract_inverted_index.Therefore, | 112 |
| abstract_inverted_index.automating | 120 |
| abstract_inverted_index.automation | 201 |
| abstract_inverted_index.meaningful | 15 |
| abstract_inverted_index.nsynthesis | 21 |
| abstract_inverted_index.previously | 79 |
| abstract_inverted_index.processing | 73 |
| abstract_inverted_index.real-time. | 92 |
| abstract_inverted_index.worst-case | 186 |
| abstract_inverted_index.HyperModels | 272 |
| abstract_inverted_index.application | 33 |
| abstract_inverted_index.established | 275 |
| abstract_inverted_index.instruments | 268 |
| abstract_inverted_index.parameters. | 16 |
| abstract_inverted_index.performance | 151, 170, 236 |
| abstract_inverted_index.programming | 94 |
| abstract_inverted_index.resolutions | 65, 247, 259 |
| abstract_inverted_index.HyperModels, | 116 |
| abstract_inverted_index.considerable | 44 |
| abstract_inverted_index.considerably | 84 |
| abstract_inverted_index.equivalents, | 241 |
| abstract_inverted_index.equivalents. | 163 |
| abstract_inverted_index.performance. | 222 |
| abstract_inverted_index.resolutions, | 86 |
| abstract_inverted_index.architecture, | 101 |
| abstract_inverted_index.computational | 45 |
| abstract_inverted_index.inexperienced | 110 |
| abstract_inverted_index.requirements. | 46 |
| abstract_inverted_index.understanding | 98 |
| abstract_inverted_index.Unfortunately, | 31 |
| abstract_inverted_index.auto-generated | 174, 231 |
| abstract_inverted_index.circumstances, | 199 |
| abstract_inverted_index.implementation | 140 |
| abstract_inverted_index.optimisations. | 232 |
| abstract_inverted_index.practitioners. | 111 |
| abstract_inverted_index.near-ubiquitous | 71 |
| abstract_inverted_index.representations | 25 |
| abstract_inverted_index.two-dimensional | 58 |
| abstract_inverted_index.finite-difference | 60, 125 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.04226156 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |