Meerkat: A Framework for Dynamic Graph Algorithms on GPUs
Graph algorithms are challenging to implement due to their varying topology and irregular access patterns. Real-world graphs are dynamic in nature and routinely undergo edge and vertex additions, as well as, deletions. Typical examples of dynamic graphs are social networks, collaboration networks, a...
Saved in:
| Published in | International journal of parallel programming Vol. 52; no. 5-6; pp. 400 - 453 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.12.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0885-7458 1573-7640 |
| DOI | 10.1007/s10766-024-00774-z |
Cover
| Abstract | Graph algorithms are challenging to implement due to their varying topology and irregular access patterns. Real-world graphs are dynamic in nature and routinely undergo edge and vertex additions, as well as, deletions. Typical examples of dynamic graphs are social networks, collaboration networks, and road networks. Applying static algorithms repeatedly on dynamic graphs is inefficient. Further, due to the rapid growth of unstructured and semi-structured data, graph algorithms demand efficient parallel processing. Unfortunately, we know only a little about how to efficiently process dynamic graphs on massively parallel architectures such as GPUs. Existing approaches to represent and process dynamic graphs are either not general or are inefficient. In this work, we propose a graph library for dynamic graph algorithms over a GPU-tailored graph representation and exploits the
warp-cooperative work-sharing execution model
. The library, named Meerkat, builds upon a recently proposed dynamic graph representation on GPUs. This representation exploits a hashtable-based mechanism to store a vertex’s neighborhood. Meerkat also enables fast iteration through a group of vertices, a pattern common and crucial for achieving performance in graph applications. Our framework supports dynamic edge additions and edge deletions, along with their batched versions. Based on the efficient iterative patterns encoded in Meerkat, we implement dynamic versions of popular graph algorithms such as breadth-first search, single-source shortest paths, triangle counting, PageRank, and weakly connected components. We evaluated our implementations over the ones in other publicly available dynamic graph data structures and frameworks:
GPMA
,
Hornet
, and
faimGraph
. Using a variety of real-world graphs, we observe that Meerkat significantly improves the efficiency of the underlying dynamic graph algorithm, outperforming these frameworks. |
|---|---|
| AbstractList | Graph algorithms are challenging to implement due to their varying topology and irregular access patterns. Real-world graphs are dynamic in nature and routinely undergo edge and vertex additions, as well as, deletions. Typical examples of dynamic graphs are social networks, collaboration networks, and road networks. Applying static algorithms repeatedly on dynamic graphs is inefficient. Further, due to the rapid growth of unstructured and semi-structured data, graph algorithms demand efficient parallel processing. Unfortunately, we know only a little about how to efficiently process dynamic graphs on massively parallel architectures such as GPUs. Existing approaches to represent and process dynamic graphs are either not general or are inefficient. In this work, we propose a graph library for dynamic graph algorithms over a GPU-tailored graph representation and exploits the
warp-cooperative work-sharing execution model
. The library, named Meerkat, builds upon a recently proposed dynamic graph representation on GPUs. This representation exploits a hashtable-based mechanism to store a vertex’s neighborhood. Meerkat also enables fast iteration through a group of vertices, a pattern common and crucial for achieving performance in graph applications. Our framework supports dynamic edge additions and edge deletions, along with their batched versions. Based on the efficient iterative patterns encoded in Meerkat, we implement dynamic versions of popular graph algorithms such as breadth-first search, single-source shortest paths, triangle counting, PageRank, and weakly connected components. We evaluated our implementations over the ones in other publicly available dynamic graph data structures and frameworks:
GPMA
,
Hornet
, and
faimGraph
. Using a variety of real-world graphs, we observe that Meerkat significantly improves the efficiency of the underlying dynamic graph algorithm, outperforming these frameworks. Graph algorithms are challenging to implement due to their varying topology and irregular access patterns. Real-world graphs are dynamic in nature and routinely undergo edge and vertex additions, as well as, deletions. Typical examples of dynamic graphs are social networks, collaboration networks, and road networks. Applying static algorithms repeatedly on dynamic graphs is inefficient. Further, due to the rapid growth of unstructured and semi-structured data, graph algorithms demand efficient parallel processing. Unfortunately, we know only a little about how to efficiently process dynamic graphs on massively parallel architectures such as GPUs. Existing approaches to represent and process dynamic graphs are either not general or are inefficient. In this work, we propose a graph library for dynamic graph algorithms over a GPU-tailored graph representation and exploits the warp-cooperative work-sharing execution model. The library, named Meerkat, builds upon a recently proposed dynamic graph representation on GPUs. This representation exploits a hashtable-based mechanism to store a vertex’s neighborhood. Meerkat also enables fast iteration through a group of vertices, a pattern common and crucial for achieving performance in graph applications. Our framework supports dynamic edge additions and edge deletions, along with their batched versions. Based on the efficient iterative patterns encoded in Meerkat, we implement dynamic versions of popular graph algorithms such as breadth-first search, single-source shortest paths, triangle counting, PageRank, and weakly connected components. We evaluated our implementations over the ones in other publicly available dynamic graph data structures and frameworks: GPMA, Hornet, and faimGraph. Using a variety of real-world graphs, we observe that Meerkat significantly improves the efficiency of the underlying dynamic graph algorithm, outperforming these frameworks. |
| Author | Concessao, Kevin Jude Nasre, Rupesh Cheramangalath, Unnikrishnan Dev, Ricky |
| Author_xml | – sequence: 1 givenname: Kevin Jude surname: Concessao fullname: Concessao, Kevin Jude email: 112014001@smail.iitpkd.ac.in organization: Department of Computer Science and Engineering, Indian Institute of Technology Palakkad – sequence: 2 givenname: Unnikrishnan surname: Cheramangalath fullname: Cheramangalath, Unnikrishnan organization: Department of Computer Science and Engineering, Indian Institute of Technology Palakkad – sequence: 3 givenname: Ricky surname: Dev fullname: Dev, Ricky organization: Department of Computer Science and Engineering, Indian Institute of Technology Madras – sequence: 4 givenname: Rupesh surname: Nasre fullname: Nasre, Rupesh organization: Department of Computer Science and Engineering, Indian Institute of Technology Madras |
| BookMark | eNp9kE9PwjAYhxuDiYB-AU9NPFffrtvaeiMoaILRg5ybdnQwYCu2IwY-vdWZmHjg1LfJ73n_PAPUa1xjEbqmcEsB-F2gwPOcQJKS-OUpOZ6hPs04IzxPoYf6IERGeJqJCzQIYQ0AkgvRR_LFWr_R7T0e4YnXtf10foNL5_HDodF1VeCp17sVHm2Xzlftqg7YNXj6Ng-X6LzU22Cvft8hmk8e38dPZPY6fR6PZqRgVLbEJloKa23JeKEBDI-VLhcySyHJC2N1ZrVMjUklN5JzTqVZlGxhmABji3jBEN10fXfefextaNXa7X0TRypGKVAmZJ7FlOhShXcheFuqomp1W7mm9braKgrqW5TqRKkoSv2IUseIJv_Qna9q7Q-nIdZBIYabpfV_W52gvgBIpH1i |
| CitedBy_id | crossref_primary_10_3390_app15063172 |
| Cites_doi | 10.1109/IPDPS.2018.00052 10.1007/978-3-030-35225-7_17 10.1145/1150402.1150412 10.14778/3514061.3514065 10.1145/3364180 10.1109/IPDPSW.2010.5470817 10.1109/HPEC.2018.8547541 10.1016/0304-3975(95)00079-8 10.1145/3208040.3208041 10.1145/3037697.3037748 10.1145/1142351.1142355 10.14778/3384345.3384351 10.1080/15427951.2009.10129177 10.14778/3436905.3436923 10.1109/SC41404.2022.00050 10.1007/978-0-387-30162-4_61 10.1109/IPDPS.2018.00012 10.1109/HPEC.2017.8091058 10.1109/TKDE.2022.3171588 10.1109/IPDPS47924.2020.00081 10.1109/HiPC.2017.00011 10.1109/HPEC.2016.7761622 10.1007/978-3-030-90888-1_36 10.1145/3448016.3457313 10.1007/978-3-030-41886-1_6 10.1109/HPEC.2012.6408680 10.1038/srep02980 10.1145/3314221.3314598 10.1109/IPDPS.2019.00110 10.1145/2660193.2660227 10.1109/SC.2018.00063 10.1145/2350190.2350193 10.1145/2442516.2442530 10.1145/3575693.3575713 10.1145/1753326.1753532 10.1145/3410463.3414657 10.14778/3447689.3447708 10.14778/3151113.3151122 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1007/s10766-024-00774-z |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1573-7640 |
| EndPage | 453 |
| ExternalDocumentID | 10_1007_s10766_024_00774_z |
| GrantInformation_xml | – fundername: National Super Computing Mission, India grantid: DST/NSM/R&D Exascale/2021/18 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C -~X .4S .86 .DC .VR 06D 0R~ 0VY 199 1N0 2.D 203 28- 29J 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 78A 7WY 8FE 8FG 8FL 8G5 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYJJ AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDPE ABDZT ABECU ABFSI ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTAH ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHXU ACIHN ACKNC ACMDZ ACMLO ACNCT ACOKC ACOMO ACPIV ACREN ACUHS ACZOJ ADHIR ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AOCGG ARAPS ARCSS ARMRJ AXYYD AYJHY AZFZN AZQEC B-. B0M BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BKOMP BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO E.L EAD EAP EAS EBLON EBS EDO EIOEI EJD EMK EPL ESBYG ESX FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GROUPED_ABI_INFORM_RESEARCH GUQSH GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ H~9 I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW LAK LLZTM M0C M0N M2O M4Y MA- MS~ N2Q NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PT5 Q2X QOK QOS R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TAE TEORI TN5 TSG TSK TSV TUC TUS U2A U5U UG4 UOJIU UTJUX UZXMN VC2 VFIZW VXZ W23 W48 WH7 WK8 YLTOR Z45 Z7R Z7X Z81 Z83 Z88 Z8R Z8W Z92 ZMTXR ZY4 ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB PUEGO 7SC 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c319t-e2a98eeef37ca00b7ef3afd954026cbea5ea94bb497b977719bdf3db380bec573 |
| IEDL.DBID | U2A |
| ISSN | 0885-7458 |
| IngestDate | Sat Jul 26 01:13:14 EDT 2025 Wed Oct 01 01:32:53 EDT 2025 Thu Apr 24 22:59:14 EDT 2025 Fri Feb 21 02:41:49 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5-6 |
| Keywords | CUDA Parallel computing GPU programming Dynamic graph algorithms |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c319t-e2a98eeef37ca00b7ef3afd954026cbea5ea94bb497b977719bdf3db380bec573 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 3110138965 |
| PQPubID | 48389 |
| PageCount | 54 |
| ParticipantIDs | proquest_journals_3110138965 crossref_citationtrail_10_1007_s10766_024_00774_z crossref_primary_10_1007_s10766_024_00774_z springer_journals_10_1007_s10766_024_00774_z |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20241200 2024-12-00 20241201 |
| PublicationDateYYYYMMDD | 2024-12-01 |
| PublicationDate_xml | – month: 12 year: 2024 text: 20241200 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | International journal of parallel programming |
| PublicationTitleAbbrev | Int J Parallel Prog |
| PublicationYear | 2024 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. CHI ’10, 1361–1370 (Association for Computing Machinery, New York, NY, USA, 2010). https://doi.org/10.1145/1753326.1753532 Winter, M., Zayer, R., Steinberger, M.: Autonomous, independent management of dynamic graphs on gpus. In: 2017 IEEE High Performance Extreme Computing Conference (HPEC), 1–7 (2017). https://doi.org/10.1109/HPEC.2017.8091058 Ashkiani, S., Farach-Colton, M., Owens, J. D.: A dynamic hash table for the GPU. In: 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 419–429 (2018). https://doi.org/10.1109/IPDPS.2018.00052 Ediger, D., McColl, R., Riedy, J., Bader, D. A.: Stinger: high performance data structure for streaming graphs. In: 2012 IEEE Conference on High Performance Extreme Computing, 1–5 (2012). https://doi.org/10.1109/HPEC.2012.6408680 Jaiyeoba, W., Skadron, K.: Graphtinker: a high performance data structure for dynamic graph processing. In: 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 1030–1041 (2019). https://doi.org/10.1109/IPDPS.2019.00110 KumarPHuangHHGraphone: a data store for real-time analytics on evolving graphsACM Trans. Storage202015414010.1145/3364180 Shun, J., Blelloch, G. E.: Ligra: a lightweight graph processing framework for shared memory. In: Proceedings of the 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP ’13, 135–146 (Association for Computing Machinery, New York, NY, USA), (2013)https://doi.org/10.1145/2442516.2442530 Zhang, F., Zou, L., Yu, Y. Zhang, W., Zou, L., Maamar, Z., Chen, L.: (eds) Lpma - an efficient data structure for dynamic graph on gpus. Web Information Systems Engineering – WISE 2021, 469–484 (Springer International Publishing, Cham), (2021) CheramangalathUNasreRSrikantYNDynamic Graph Algorithms2020BerlinSpringer International Publishing13715110.1007/978-3-030-41886-1_6 De LeoDBonczPTeseo and the analysis of structural dynamic graphsProc. VLDB Endow.2021141053106610.14778/3447689.3447708 Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’06, 44–54 (Association for Computing Machinery, New York, NY, USA), (2006). https://doi.org/10.1145/1150402.1150412 CUDA Pro Tip: Write Flexible Kernels with Grid-Stride Loops | NVIDIA Technical Blog — developer.nvidia.com. https://developer.nvidia.com/blog/cuda-pro-tip-write-flexible-kernels-grid-stride-loops/. [Accessed 08-11-2023] ZhangYEgraph: efficient concurrent GPU-based dynamic graph processingIEEE Transactions on Knowledge and Data Engineering2023355823583610.1109/TKDE.2022.3171588 Jaiganesh, J., Burtscher, M.: A high-performance connected components implementation for gpus. In: Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing, HPDC ’18, 92–104 (Association for Computing Machinery, New York, NY, USA), (2018). https://doi.org/10.1145/3208040.3208041 DIMACS Implementation Challege. http://www.diag.uniroma1.it//challenge9/download.shtml Bender, M. A., Hu, H.: An adaptive packed-memory array. In: Proceedings of the Twenty-Fifth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS ’06, 20–29 (Association for Computing Machinery, New York, NY, USA), (2006). https://doi.org/10.1145/1142351.1142355 Soman, J., Kishore, K., Narayanan, P. J.: A fast gpu algorithm for graph connectivity. In: 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 1–8 (2010). https://doi.org/10.1109/IPDPSW.2010.5470817 Nvidia. Nvidia warp primitives. https://developer.nvidia.com/blog/using-cuda-warp-level-primitives Fagerberg, R.: Cache-Oblivious B-Tree, 121–123 (Springer US, Boston, MA), (2008). https://doi.org/10.1007/978-0-387-30162-4_61 Busato, F., Green, O., Bombieri, N., Bader, D. A.: Hornet: an efficient data structure for dynamic sparse graphs and matrices on gpus. In: 2018 IEEE High Performance extreme Computing Conference (HPEC), 1–7 (IEEE), (2018). https://doi.org/10.1109/HPEC.2018.8547541 Malhotra, G., Chappidi, H., Nasre, R. Rauchwerger, L.: (ed.) Fast Dynamic Graph Algorithms. Languages and Compilers for Parallel Computing - 30th International Workshop, LCPC 2017, College Station, TX, USA, October 11-13, 2017, Revised Selected Papers, Vol. 11403 of Lecture Notes in Computer Science, 262–277 (Springer), (2017). https://doi.org/10.1007/978-3-030-35225-7_17 ShaMLiYHeBTanK-LAccelerating dynamic graph analytics on GPUsProc. VLDB Endow.20171110712010.14778/3151113.3151122 Vora, K., Koduru, S. C., Gupta, R.: Aspire: exploiting asynchronous parallelism in iterative algorithms using a relaxed consistency based DSM. In: Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications, OOPSLA ’14, 861–878 (Association for Computing Machinery, New York, NY, USA), (2014). https://doi.org/10.1145/2660193.2660227 CUDA C++ Programming Guide - Cooperative Groups (2023). https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#cooperative-groups. [Accessed 08-11-2023] Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, MDS ’12 (Association for Computing Machinery, New York, NY, USA), (2012). https://doi.org/10.1145/2350190.2350193 Dhulipala, L., Blelloch, G. E., Shun, J.: Low-latency graph streaming using compressed purely-functional trees. In: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2019, 918–934 (Association for Computing Machinery, New York, NY, USA), (2019). https://doi.org/10.1145/3314221.3314598 Awad, M.A., Ashkiani, S., Porumbescu, S.D., Owens, J.D.: Dynamic graphs on the GPU (2020). https://doi.org/10.1109/IPDPS47924.2020.00081 Arasu, A., Novak, J., Tomkins, A., Tomlin, J. A.: Pagerank computation and the structure of the web: experiments and algorithms (2002) DhulipalaLHongCShunJConnectit: a framework for static and incremental parallel graph connectivity algorithmsProc. VLDB Endow.20201465366710.14778/3436905.3436923 Makkar, D., Bader, D. A., Green, O.: Exact and parallel triangle counting in dynamic graphs. In: 2017 IEEE 24th International Conference on High Performance Computing (HiPC), 2–12 (2017). https://doi.org/10.1109/HiPC.2017.00011 Afarin, M., Gao, C., Rahman, S., Abu-Ghazaleh, N., Gupta, R.: Commongraph: graph analytics on evolving data. In: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, ASPLOS 2023, 133–145 (Association for Computing Machinery, New York, NY, USA), (2023). https://doi.org/10.1145/3575693.3575713 Winter, M., Mlakar, D., Zayer, R., Seidel, H.-P., Steinberger, M.: Faimgraph: high performance management of fully-dynamic graphs under tight memory constraints on the gpu. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC ’18 (IEEE Press), (2018) Pandey, P., Wheatman, B., Xu, H., Buluc, A.: Terrace: a hierarchical graph container for skewed dynamic graphs. In: Proceedings of the 2021 International Conference on Management of Data, SIGMOD ’21, 1372–1385 (Association for Computing Machinery, New York, NY, USA), (2021). https://doi.org/10.1145/3448016.3457313 De DomenicoMLimaAMougelPMusolesiMThe anatomy of a scientific rumorScientific reports201331298010.1038/srep02980 Takac, L., Zábovský, M.: Data analysis in public social networks. In: International Scientific Conference and International Workshop Present Day Trends of Innovations 1–6 (2012) Green, O., Bader, D. A.: cuSTINGER: supporting dynamic graph algorithms for gpus. In: 2016 IEEE High Performance Extreme Computing Conference (HPEC), 1–6 (2016). https://doi.org/10.1109/HPEC.2016.7761622 Vora, K., Gupta, R., Xu, G.: Kickstarter: fast and accurate computations on streaming graphs via trimmed approximations. In: Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS ’17, 237–251 (Association for Computing Machinery, New York, NY, USA), (2017). https://doi.org/10.1145/3037697.3037748 ZhuXLivegraph: a transactional graph storage system with purely sequential adjacency list scansProc. VLDB Endow2020131020103410.14778/3384345.3384351 Leskovec, J., Lang, K. J., Dasgupta, A., Mahoney, M. W.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters (2008). arXiv:0810.1355 Chen, D., et al.: Graphfly: efficient asynchronous streaming graphs processing via dependency-flow. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC ’22 (IEEE Press, 2022) Hong, C., Dhulipala, L., Shun, J.: Exploring the design space of static and incremental graph connectivity algorithms on gpus. Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques, PACT ’20, 55–69 (Association for Computing Machinery, New York, NY, USA), (2020). https://doi.org/10.1145/3410463.3414657 FuchsPMarganDGicevaJSortledton: a universal, transactional graph data structureProc. VLDB Endow.2022151173118610.14778/3514061.3514065 Sutton, M., Ben-Nun, T., Barak, A.: Optimizing parallel graph connectivity computation via subgraph sampling. In: 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 12–21 (2018). https://doi.org/10.1109/IPDPS.2018.00012 RamalingamGRepsTOn the computational complexity of dynamic graph problemsTheor. Comput. Sci.1996158233277138897210.1016/0304-3975(95)00079-8 D De Leo (774_CR25) 2021; 14 M Sha (774_CR8) 2017; 11 U Cheramangalath (774_CR42) 2020 G Ramalingam (774_CR43) 1996; 158 774_CR18 774_CR17 774_CR39 Y Zhang (774_CR33) 2023; 35 774_CR19 774_CR27 774_CR21 774_CR20 774_CR23 774_CR44 774_CR41 774_CR40 774_CR4 774_CR5 774_CR2 774_CR3 774_CR9 774_CR6 774_CR7 P Fuchs (774_CR26) 2022; 15 774_CR29 L Dhulipala (774_CR38) 2020; 14 774_CR1 774_CR28 P Kumar (774_CR24) 2020; 15 774_CR36 774_CR13 774_CR35 774_CR16 774_CR15 774_CR37 774_CR10 774_CR32 774_CR31 774_CR12 774_CR34 774_CR11 774_CR30 X Zhu (774_CR22) 2020; 13 M De Domenico (774_CR14) 2013; 3 |
| References_xml | – reference: Leskovec, J., Lang, K. J., Dasgupta, A., Mahoney, M. W.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters (2008). arXiv:0810.1355 – reference: Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, MDS ’12 (Association for Computing Machinery, New York, NY, USA), (2012). https://doi.org/10.1145/2350190.2350193 – reference: Arasu, A., Novak, J., Tomkins, A., Tomlin, J. A.: Pagerank computation and the structure of the web: experiments and algorithms (2002) – reference: Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’06, 44–54 (Association for Computing Machinery, New York, NY, USA), (2006). https://doi.org/10.1145/1150402.1150412 – reference: Pandey, P., Wheatman, B., Xu, H., Buluc, A.: Terrace: a hierarchical graph container for skewed dynamic graphs. In: Proceedings of the 2021 International Conference on Management of Data, SIGMOD ’21, 1372–1385 (Association for Computing Machinery, New York, NY, USA), (2021). https://doi.org/10.1145/3448016.3457313 – reference: Chen, D., et al.: Graphfly: efficient asynchronous streaming graphs processing via dependency-flow. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC ’22 (IEEE Press, 2022) – reference: Vora, K., Koduru, S. C., Gupta, R.: Aspire: exploiting asynchronous parallelism in iterative algorithms using a relaxed consistency based DSM. In: Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications, OOPSLA ’14, 861–878 (Association for Computing Machinery, New York, NY, USA), (2014). https://doi.org/10.1145/2660193.2660227 – reference: CUDA C++ Programming Guide - Cooperative Groups (2023). https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#cooperative-groups. [Accessed 08-11-2023] – reference: De LeoDBonczPTeseo and the analysis of structural dynamic graphsProc. VLDB Endow.2021141053106610.14778/3447689.3447708 – reference: Jaiganesh, J., Burtscher, M.: A high-performance connected components implementation for gpus. In: Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing, HPDC ’18, 92–104 (Association for Computing Machinery, New York, NY, USA), (2018). https://doi.org/10.1145/3208040.3208041 – reference: CheramangalathUNasreRSrikantYNDynamic Graph Algorithms2020BerlinSpringer International Publishing13715110.1007/978-3-030-41886-1_6 – reference: Ediger, D., McColl, R., Riedy, J., Bader, D. A.: Stinger: high performance data structure for streaming graphs. In: 2012 IEEE Conference on High Performance Extreme Computing, 1–5 (2012). https://doi.org/10.1109/HPEC.2012.6408680 – reference: Green, O., Bader, D. A.: cuSTINGER: supporting dynamic graph algorithms for gpus. In: 2016 IEEE High Performance Extreme Computing Conference (HPEC), 1–6 (2016). https://doi.org/10.1109/HPEC.2016.7761622 – reference: Jaiyeoba, W., Skadron, K.: Graphtinker: a high performance data structure for dynamic graph processing. In: 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 1030–1041 (2019). https://doi.org/10.1109/IPDPS.2019.00110 – reference: Dhulipala, L., Blelloch, G. E., Shun, J.: Low-latency graph streaming using compressed purely-functional trees. In: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2019, 918–934 (Association for Computing Machinery, New York, NY, USA), (2019). https://doi.org/10.1145/3314221.3314598 – reference: DIMACS Implementation Challege. http://www.diag.uniroma1.it//challenge9/download.shtml – reference: Shun, J., Blelloch, G. E.: Ligra: a lightweight graph processing framework for shared memory. In: Proceedings of the 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP ’13, 135–146 (Association for Computing Machinery, New York, NY, USA), (2013)https://doi.org/10.1145/2442516.2442530 – reference: Takac, L., Zábovský, M.: Data analysis in public social networks. In: International Scientific Conference and International Workshop Present Day Trends of Innovations 1–6 (2012) – reference: ZhangYEgraph: efficient concurrent GPU-based dynamic graph processingIEEE Transactions on Knowledge and Data Engineering2023355823583610.1109/TKDE.2022.3171588 – reference: Makkar, D., Bader, D. A., Green, O.: Exact and parallel triangle counting in dynamic graphs. In: 2017 IEEE 24th International Conference on High Performance Computing (HiPC), 2–12 (2017). https://doi.org/10.1109/HiPC.2017.00011 – reference: Ashkiani, S., Farach-Colton, M., Owens, J. D.: A dynamic hash table for the GPU. In: 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 419–429 (2018). https://doi.org/10.1109/IPDPS.2018.00052 – reference: FuchsPMarganDGicevaJSortledton: a universal, transactional graph data structureProc. VLDB Endow.2022151173118610.14778/3514061.3514065 – reference: KumarPHuangHHGraphone: a data store for real-time analytics on evolving graphsACM Trans. Storage202015414010.1145/3364180 – reference: Malhotra, G., Chappidi, H., Nasre, R. Rauchwerger, L.: (ed.) Fast Dynamic Graph Algorithms. Languages and Compilers for Parallel Computing - 30th International Workshop, LCPC 2017, College Station, TX, USA, October 11-13, 2017, Revised Selected Papers, Vol. 11403 of Lecture Notes in Computer Science, 262–277 (Springer), (2017). https://doi.org/10.1007/978-3-030-35225-7_17 – reference: De DomenicoMLimaAMougelPMusolesiMThe anatomy of a scientific rumorScientific reports201331298010.1038/srep02980 – reference: RamalingamGRepsTOn the computational complexity of dynamic graph problemsTheor. Comput. Sci.1996158233277138897210.1016/0304-3975(95)00079-8 – reference: Vora, K., Gupta, R., Xu, G.: Kickstarter: fast and accurate computations on streaming graphs via trimmed approximations. In: Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS ’17, 237–251 (Association for Computing Machinery, New York, NY, USA), (2017). https://doi.org/10.1145/3037697.3037748 – reference: DhulipalaLHongCShunJConnectit: a framework for static and incremental parallel graph connectivity algorithmsProc. VLDB Endow.20201465366710.14778/3436905.3436923 – reference: Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. CHI ’10, 1361–1370 (Association for Computing Machinery, New York, NY, USA, 2010). https://doi.org/10.1145/1753326.1753532 – reference: Zhang, F., Zou, L., Yu, Y. Zhang, W., Zou, L., Maamar, Z., Chen, L.: (eds) Lpma - an efficient data structure for dynamic graph on gpus. Web Information Systems Engineering – WISE 2021, 469–484 (Springer International Publishing, Cham), (2021) – reference: ShaMLiYHeBTanK-LAccelerating dynamic graph analytics on GPUsProc. VLDB Endow.20171110712010.14778/3151113.3151122 – reference: Bender, M. A., Hu, H.: An adaptive packed-memory array. In: Proceedings of the Twenty-Fifth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS ’06, 20–29 (Association for Computing Machinery, New York, NY, USA), (2006). https://doi.org/10.1145/1142351.1142355 – reference: Fagerberg, R.: Cache-Oblivious B-Tree, 121–123 (Springer US, Boston, MA), (2008). https://doi.org/10.1007/978-0-387-30162-4_61 – reference: Winter, M., Zayer, R., Steinberger, M.: Autonomous, independent management of dynamic graphs on gpus. In: 2017 IEEE High Performance Extreme Computing Conference (HPEC), 1–7 (2017). https://doi.org/10.1109/HPEC.2017.8091058 – reference: Busato, F., Green, O., Bombieri, N., Bader, D. A.: Hornet: an efficient data structure for dynamic sparse graphs and matrices on gpus. In: 2018 IEEE High Performance extreme Computing Conference (HPEC), 1–7 (IEEE), (2018). https://doi.org/10.1109/HPEC.2018.8547541 – reference: Hong, C., Dhulipala, L., Shun, J.: Exploring the design space of static and incremental graph connectivity algorithms on gpus. Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques, PACT ’20, 55–69 (Association for Computing Machinery, New York, NY, USA), (2020). https://doi.org/10.1145/3410463.3414657 – reference: Afarin, M., Gao, C., Rahman, S., Abu-Ghazaleh, N., Gupta, R.: Commongraph: graph analytics on evolving data. In: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, ASPLOS 2023, 133–145 (Association for Computing Machinery, New York, NY, USA), (2023). https://doi.org/10.1145/3575693.3575713 – reference: CUDA Pro Tip: Write Flexible Kernels with Grid-Stride Loops | NVIDIA Technical Blog — developer.nvidia.com. https://developer.nvidia.com/blog/cuda-pro-tip-write-flexible-kernels-grid-stride-loops/. [Accessed 08-11-2023] – reference: Winter, M., Mlakar, D., Zayer, R., Seidel, H.-P., Steinberger, M.: Faimgraph: high performance management of fully-dynamic graphs under tight memory constraints on the gpu. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, SC ’18 (IEEE Press), (2018) – reference: Nvidia. Nvidia warp primitives. https://developer.nvidia.com/blog/using-cuda-warp-level-primitives/ – reference: ZhuXLivegraph: a transactional graph storage system with purely sequential adjacency list scansProc. VLDB Endow2020131020103410.14778/3384345.3384351 – reference: Soman, J., Kishore, K., Narayanan, P. J.: A fast gpu algorithm for graph connectivity. In: 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 1–8 (2010). https://doi.org/10.1109/IPDPSW.2010.5470817 – reference: Awad, M.A., Ashkiani, S., Porumbescu, S.D., Owens, J.D.: Dynamic graphs on the GPU (2020). https://doi.org/10.1109/IPDPS47924.2020.00081 – reference: Sutton, M., Ben-Nun, T., Barak, A.: Optimizing parallel graph connectivity computation via subgraph sampling. In: 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 12–21 (2018). https://doi.org/10.1109/IPDPS.2018.00012 – ident: 774_CR6 doi: 10.1109/IPDPS.2018.00052 – ident: 774_CR1 doi: 10.1007/978-3-030-35225-7_17 – ident: 774_CR15 doi: 10.1145/1150402.1150412 – volume: 15 start-page: 1173 year: 2022 ident: 774_CR26 publication-title: Proc. VLDB Endow. doi: 10.14778/3514061.3514065 – ident: 774_CR20 – volume: 15 start-page: 1 issue: 4 year: 2020 ident: 774_CR24 publication-title: ACM Trans. Storage doi: 10.1145/3364180 – ident: 774_CR39 doi: 10.1109/IPDPSW.2010.5470817 – ident: 774_CR4 doi: 10.1109/HPEC.2018.8547541 – volume: 158 start-page: 233 year: 1996 ident: 774_CR43 publication-title: Theor. Comput. Sci. doi: 10.1016/0304-3975(95)00079-8 – ident: 774_CR16 – ident: 774_CR40 doi: 10.1145/3208040.3208041 – ident: 774_CR11 doi: 10.1145/3037697.3037748 – ident: 774_CR35 doi: 10.1145/1142351.1142355 – volume: 13 start-page: 1020 year: 2020 ident: 774_CR22 publication-title: Proc. VLDB Endow doi: 10.14778/3384345.3384351 – ident: 774_CR12 – ident: 774_CR17 doi: 10.1080/15427951.2009.10129177 – volume: 14 start-page: 653 year: 2020 ident: 774_CR38 publication-title: Proc. VLDB Endow. doi: 10.14778/3436905.3436923 – ident: 774_CR9 – ident: 774_CR10 – ident: 774_CR27 doi: 10.1109/SC41404.2022.00050 – ident: 774_CR36 doi: 10.1007/978-0-387-30162-4_61 – ident: 774_CR41 doi: 10.1109/IPDPS.2018.00012 – ident: 774_CR32 doi: 10.1109/HPEC.2017.8091058 – volume: 35 start-page: 5823 year: 2023 ident: 774_CR33 publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2022.3171588 – ident: 774_CR2 doi: 10.1109/IPDPS47924.2020.00081 – ident: 774_CR13 doi: 10.1109/HiPC.2017.00011 – ident: 774_CR5 doi: 10.1109/HPEC.2016.7761622 – ident: 774_CR31 doi: 10.1007/978-3-030-90888-1_36 – ident: 774_CR28 doi: 10.1145/3448016.3457313 – start-page: 137 volume-title: Dynamic Graph Algorithms year: 2020 ident: 774_CR42 doi: 10.1007/978-3-030-41886-1_6 – ident: 774_CR7 doi: 10.1109/HPEC.2012.6408680 – volume: 3 start-page: 2980 issue: 1 year: 2013 ident: 774_CR14 publication-title: Scientific reports doi: 10.1038/srep02980 – ident: 774_CR29 doi: 10.1145/3314221.3314598 – ident: 774_CR23 doi: 10.1109/IPDPS.2019.00110 – ident: 774_CR34 doi: 10.1145/2660193.2660227 – ident: 774_CR3 doi: 10.1109/SC.2018.00063 – ident: 774_CR19 doi: 10.1145/2350190.2350193 – ident: 774_CR37 doi: 10.1145/2442516.2442530 – ident: 774_CR30 doi: 10.1145/3575693.3575713 – ident: 774_CR18 doi: 10.1145/1753326.1753532 – ident: 774_CR21 doi: 10.1145/3410463.3414657 – volume: 14 start-page: 1053 year: 2021 ident: 774_CR25 publication-title: Proc. VLDB Endow. doi: 10.14778/3447689.3447708 – ident: 774_CR44 – volume: 11 start-page: 107 year: 2017 ident: 774_CR8 publication-title: Proc. VLDB Endow. doi: 10.14778/3151113.3151122 |
| SSID | ssj0009788 |
| Score | 2.3443604 |
| Snippet | Graph algorithms are challenging to implement due to their varying topology and irregular access patterns. Real-world graphs are dynamic in nature and... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 400 |
| SubjectTerms | Algorithms Apexes Computer Science Cooperative work Data structures Graph representations Graph theory Graphical representations Graphics processing units Parallel processing Processor Architectures Search algorithms Search engines Shortest-path problems Social networks Software Engineering/Programming and Operating Systems Structured data Theory of Computation Topology Unstructured data |
| Title | Meerkat: A Framework for Dynamic Graph Algorithms on GPUs |
| URI | https://link.springer.com/article/10.1007/s10766-024-00774-z https://www.proquest.com/docview/3110138965 |
| Volume | 52 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVLSH databaseName: SpringerLink Journals customDbUrl: mediaType: online eissn: 1573-7640 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009788 issn: 0885-7458 databaseCode: AFBBN dateStart: 19970201 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 1573-7640 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009788 issn: 0885-7458 databaseCode: AGYKE dateStart: 19970101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 1573-7640 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0009788 issn: 0885-7458 databaseCode: U2A dateStart: 19970101 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT4MwFG50u3jxt3E6lx68aRNGKaXeiLotmhkPkswTactDjZOZgZf99bYMRI2aeCNQevj6PvqRvvc9hI5l4FDFDZG4pJR4llJSpECMsgXRZ1qoskJufOOPIu9qwiZVUVheZ7vXR5Lll_pTsRv3bcKsR6wHjUcWq6jNrJ2XieLIDRurXV52mzT0YYR7LKhKZX6e4-t21GjMb8ei5W4z2ETrlUzE4XJdt9AKZNtoo27BgCtG7iAxBpg_y-IMh3hQ51lhI0TxxbLVPB5aR2ocTh9m86fi8SXHswwPb6N8F0WDy7vzEam6IRBtaFIQcKUIACClXEvHUdxcyTQRRnK5vlYgGUjhKeUJroyo432hkpQmigaOWSfG6R5qZbMM9hHWHAKmHfBVyjxKmUqFVuaOBPM_nNCgg_o1KLGurMJtx4pp3JgcWyBjA2RcAhkvOujk453XpVHGn6O7NdZxRZo8pkaK2HNTn3XQaY1_8_j32Q7-N_wQrbk2BMqklC5qFfM3ODLSolA91A6H99eXvTKi3gEeLMXh |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NT8IwFH9ROOhF_Iwoag_edGTQdl29LcqH8hEPkOBpaUunBgQD48Jfb_clStTE27J1zfo-1l_z3vs9gEvh2lgy40hMYGyRyKUED7RlkK3mFaq4jCvkOl2n2ScPAzpIi8LmWbZ7FpKM_9Rfit2YEyXMEivioCHWchPyxBxQqjnIe42nVm1FtsvifpPGgajFCHXTYpmfZ_m-Ia1Q5lpgNN5v6gXoZ1-apJmMyotQltVyjcTxv0vZhZ0UgCIvsZg92NCTfShkzR1Q6usHwDtaz0YivEEeqmcZXMhAXHSXNLFHjYjrGnnj5-nsNXx5m6PpBDUe-_ND6NdrvdumlfZZsJRxwNDSVcFdrXWAmRK2LZm5EsGQGzBXdZTUgmrBiZSEM2ngIqtwOQzwUGLXNhZAGT6C3GQ60ceAFNMuVbZ2ZEAJxlQGXElzR2hz0h5itwiVTNi-SknIo14YY39FnxzJxjey8WPZ-MsiXH2-855QcPw5upTp0E_dce5jA3KiiKxDi3CdqWT1-PfZTv43_AK2mr1O22_fd1unsF2NNBynvpQgF84W-swAmFCep_b6AVWP5Hc |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV27TsMwFLWgSIiFN6JQwAMbWE1jO47ZIkpaHq06UKlbZCcOIEpatWHp12PnQQoCJLYosT0c3yOfyPeeC8C5cC0smSYSExgjYigleKyQVraKt2jIZVYh1-s73SG5G9HRUhV_lu1eXknmNQ3GpSlJm9Mobi4VvjHHJM8SZPxoCFqsgjVijBJ0RA9tr7LdZVnnSU0lihihblE28_MaX4-mSm9-uyLNTh5_G2wWkhF6-R7vgBWV7IKtsh0DLNi5B3hPqdmrSK-gB_0y5wpqUQrbedt52DHu1NAbP01mL-nz2xxOEtgZDOf7YOjfPF53UdEZAYWaMilStuCuUirGLBSWJZl-EnHEtfyynVAqQZXgRErCmdQCj7W4jGIcSexaes8owweglkwSdQhgyJRLQ0s5MqYEYypjHkr9Rij9bxxhtw5aJShBWNiGm-4V46AyPDZABhrIIAMyWNTBxeecaW6a8efoRol1UBBoHmAtS8wdqkPr4LLEv_r8-2pH_xt-BtYHbT94uO3fH4MN20RDlqvSALV09q5OtOJI5WkWVB8Z2ct1 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Meerkat%3A+A+Framework+for+Dynamic+Graph+Algorithms+on+GPUs&rft.jtitle=International+journal+of+parallel+programming&rft.au=Concessao%2C+Kevin+Jude&rft.au=Cheramangalath%2C+Unnikrishnan&rft.au=Dev%2C+Ricky&rft.au=Nasre%2C+Rupesh&rft.date=2024-12-01&rft.pub=Springer+US&rft.issn=0885-7458&rft.eissn=1573-7640&rft.volume=52&rft.issue=5-6&rft.spage=400&rft.epage=453&rft_id=info:doi/10.1007%2Fs10766-024-00774-z&rft.externalDocID=10_1007_s10766_024_00774_z |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0885-7458&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0885-7458&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0885-7458&client=summon |