Graph Dataset for "Jet: Multilevel Graph Partitioning on Graphics Processing Units"
Notice bibliographique
Résumé
*This README file was made on 2024-05-08 by Michael S. Gilbert* This dataset is a compressed archive of 65 graphs in the METIS graph file format. These graphs have been used to test and measure the capabilities of Jet, a novel hiqh-quality, GPU-parallel, k-way refinement algorithm (https://github.com/sandialabs/Jet-Partitioner). Methodological information can be found in our related publication below. ## Author Information: * Michael S. Gilbert, msg5334@psu.edu. Pennsylvania State University, University Park, USA. * Kamesh Madduri, madduri@psu.edu. Pennsylvania State University, University Park, USA. * Erik G. Boman, egboman@sandia.gov. Sandia National Laboratories, Albuquerque, USA. * Sivasankaran Rajamanickam, srajama@sandia.gov. Sandia National Laboratories, Albuquerque, USA. ## Funders and Sponsors Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. ## Sharing/Access Information ### License No copyright retained - U.S. Public Domain. ### Recommended citation for this data Gilbert, Michael; Madduri, Kamesh; Boman, Erik; Rajamanickam, Sivasankaran (2024). Graph Dataset for "Jet: Multilevel Graph Partitioning on Graphics Processing Units" [Data set]. Scholarsphere. https://doi.org/10.26207/pffm-mc36. ### Related publications Gilbert, Michael S., et al. Jet: Multilevel Graph Partitioning on Graphics Processing Units. 2023. DOI.org (Datacite), https://doi.org/10.48550/ARXIV.2304.13194. ## Data & File Overview ### File list The compression utility used to compress the graphs is "xz". The cksum output for the compressed archive is "731682270 7880707444 graphs.tar.xz". We preprocessed all graphs by performing the following steps: remove self-loops, convert all directed edges to undirected edges, remove duplicate edges, and extract the largest connected component. The following are the sources for each graph: 1. Originally created graphs: * grid_3.graph, 2000x4000 rectangular mesh (grid) * cube_2.graph, 200x200x200 cubic mesh (cube) 2. Suitesparse graph repository - T. A. Davis and Y. Hu, The University of Florida sparse matrix collection, ACM Trans. on Mathematical Software, 38 (2011). https://dl.acm.org/doi/10.1145/2049662.2049663. We included all graphs (except the mawi graphs) with at least 50 million nonzeroes but less than 750 million nonzeroes: * Bump_2911.graph * Cube\_Coup\_dt0.graph * Cube\_Coup\_dt6.graph * Flan_1565.graph * Geo_1438.graph * HV15R.graph * Hook_1498.graph * Long\_Coup\_dt0.graph * Long\_Coup\_dt6.graph * ML_Geer.graph * Queen_4147.graph * Serena.graph * af_shell10.graph * arabic-2005.graph * audikw_1.graph * cage15.graph * channel-500x100x100-b050.graph * circuit5M.graph * com-LiveJournal.graph * com-Orkut.graph * delaunay_n23.graph * delaunay_n24.graph * dielFilterV3real.graph * europe_osm.graph * hollywood-2009.graph * hugebubbles-00000.graph * hugebubbles-00010.graph * hugebubbles-00020.graph * indochina-2004.graph * kmer_A2a.graph * kmer_P1a.graph * kmer_U1a.graph * kmer_V1r.graph * kmer_V2a.graph * kron_g500-logn20.graph * kron_g500-logn21.graph * ljournal-2008.graph * mycielskian17.graph * mycielskian18.graph * nlpkkt120.graph * nlpkkt160.graph * nlpkkt200.graph * rgg\_n\_2\_22\_s0.graph * rgg\_n\_2\_23\_s0.graph * rgg\_n\_2\_24\_s0.graph * road_usa.graph * soc-LiveJournal1.graph * soc-Pokec.graph * stokes.graph * uk-2002.graph * vas\_stokes\_2M.graph * vas\_stokes\_4M.graph * vsp\_bcsstk30\_500sep\_10in\_1Kout.graph * vsp\_vibrobox\_scagr7-2c\_rlfddd.graph * wb-edu.graph 3. Miscellaneous graphs from the Open Graph Benchmark - W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec, Open Graph Benchmark: Datasets for machine learning on graphs, in Proc. Annual Conf. on Neural Inf. Proc. Systems, 2020. https://proceedings.neurips.cc/paper/2020/hash/fb60d411a5c5b72b2e7d3527cfc84fd0-Abstract.html. * citation.graph * products.graph * ppa.graph 4. Social network graphs from the Laboratory for Web Algorithmics - P. Boldi, M. Rosa, M. Santini, and S. Vigna, Layered label propagation: A multiresolution coordinate-free ordering for compressing social networks, in Proc. 20th Int’l. Conf. on World Wide Web (WWW), 2011. https://dl.acm.org/doi/10.1145/1963405.1963488. * dblp-2010.graph * amazon-2008.graph * hollywood-2011.graph * enwiki-2021.graph 5. Walshaw Graph Benchmark - A. J. Soper, C. Walshaw, and M. Cross, A combined evolutionary search and multilevel optimisation approach to graph-partitioning, Journal of Global Optimization, 29 (2004), pp. 225–241, https://api.semanticscholar.org/CorpusID:6904637. * fe_rotor.graph ## Computational Dependencies These graphs may be used with any software which supports the METIS graph file format. The graphs were later checked and verified using the `graphck` utility from METIS on a Kubuntu 22.04 x64 (5.15.0-101-generic) desktop, using cmake version 3.22.1, g++ (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0, and METIS version 5.1.0.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,087 | 0,079 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».