Engineering a Distributed-Memory Triangle Counting Algorithm
Counting triangles in a graph and incident to each vertex is a fundamental and frequently considered task of graph analysis. We consider how to efficiently do this for huge graphs using massively parallel distributed-memory machines. Unsurprisingly, the main issue is to reduce communication between...
Gespeichert in:
| Veröffentlicht in: | Proceedings - IEEE International Parallel and Distributed Processing Symposium S. 702 - 712 |
|---|---|
| Hauptverfasser: | , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
01.05.2023
|
| Schlagworte: | |
| ISSN: | 1530-2075 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Counting triangles in a graph and incident to each vertex is a fundamental and frequently considered task of graph analysis. We consider how to efficiently do this for huge graphs using massively parallel distributed-memory machines. Unsurprisingly, the main issue is to reduce communication between processors. We achieve this by counting locally whenever possible and reducing the amount of information that needs to be sent in order to handle (possible) nonlocal triangles. We also achieve linear memory requirements despite superlinear communication volume by introducing a new asynchronous sparse-all-to-all operation. Furthermore, we dramatically reduce startup overheads by allowing this communication to use indirect routing. Our algorithms scale (at least) up to 32 768 cores and are up to 18 times faster than the previous state of the art. |
|---|---|
| AbstractList | Counting triangles in a graph and incident to each vertex is a fundamental and frequently considered task of graph analysis. We consider how to efficiently do this for huge graphs using massively parallel distributed-memory machines. Unsurprisingly, the main issue is to reduce communication between processors. We achieve this by counting locally whenever possible and reducing the amount of information that needs to be sent in order to handle (possible) nonlocal triangles. We also achieve linear memory requirements despite superlinear communication volume by introducing a new asynchronous sparse-all-to-all operation. Furthermore, we dramatically reduce startup overheads by allowing this communication to use indirect routing. Our algorithms scale (at least) up to 32 768 cores and are up to 18 times faster than the previous state of the art. |
| Author | Uhl, Tim Niklas Sanders, Peter |
| Author_xml | – sequence: 1 givenname: Peter surname: Sanders fullname: Sanders, Peter email: sanders@kit.edu organization: Karlsruhe Institute of Technology,Institute of Theoretical Informatics,Karlsruhe,Germany – sequence: 2 givenname: Tim Niklas surname: Uhl fullname: Uhl, Tim Niklas email: uhl@kit.edu organization: Karlsruhe Institute of Technology,Institute of Theoretical Informatics,Karlsruhe,Germany |
| BookMark | eNotzM1Kw0AUQOFRFGxr30AhL5B45y8zF9yUtGqhYsG6LpPmThxpJjJJF317EV2dzceZsqvYR2LsnkPBOeDDervcvmuFGgsBQhYAYMoLNkeDVmqQ0pSluGQTriXkAoy-YdNh-AIQIBVO2OMqtiESpRDbzGXLMIwp1KeRmvyVuj6ds10KLrZHyqr-FMdftji2fQrjZ3fLrr07DjT_74x9PK121Uu-eXteV4tNHgSoMeelQu65clZh3dSNEI2qrSSwGrky3rhaekLXWLJKl1J4f_CI_OAMGdBWztjd3zcQ0f47hc6l854DN0ZxlD-7xUsf |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/IPDPS54959.2023.00076 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE/IET Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISBN | 9798350337662 |
| EISSN | 1530-2075 |
| EndPage | 712 |
| ExternalDocumentID | 10177419 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: European Research Council funderid: 10.13039/501100000781 |
| GroupedDBID | 29O 6IE 6IF 6IH 6IK 6IL 6IN AAJGR AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI OCL RIE RIL |
| ID | FETCH-LOGICAL-i204t-16491f14a849bdbd22d4b83e0859147f7ab3fe9ad8e845632ffcf991ca7e70583 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001035517300067&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:11:45 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i204t-16491f14a849bdbd22d4b83e0859147f7ab3fe9ad8e845632ffcf991ca7e70583 |
| PageCount | 11 |
| ParticipantIDs | ieee_primary_10177419 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-May |
| PublicationDateYYYYMMDD | 2023-05-01 |
| PublicationDate_xml | – month: 05 year: 2023 text: 2023-May |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings - IEEE International Parallel and Distributed Processing Symposium |
| PublicationTitleAbbrev | IPDPS |
| PublicationYear | 2023 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0020349 |
| Score | 1.9006737 |
| Snippet | Counting triangles in a graph and incident to each vertex is a fundamental and frequently considered task of graph analysis. We consider how to efficiently do... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 702 |
| SubjectTerms | clustering coefficient Distributed processing distributed-memory algorithm graph analysis Memory management MPI Program processors Routing Task analysis triangle counting |
| Title | Engineering a Distributed-Memory Triangle Counting Algorithm |
| URI | https://ieeexplore.ieee.org/document/10177419 |
| WOSCitedRecordID | wos001035517300067&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEA22ePBUPyp-k4PX2M0m3STgRVqLHiwLVuit5GNSC3UrdSv47022W-3Fg7eQS2CS8GaSee8hdB1SeOEgU4RpnxGeMUMUcEEsY9xmOrHU8MpsQgyHcjxWeU1Wr7gwAFA1n8FNHFZ_-W5hV_GprBOPT0BA1UANIcSarPVTXUWhlZqiQxPVecz7-XMofrqRjZJGGdMk6opsWahUCDJo_XPtfdT-5eLh_AdlDtAOFIeotTFjwPXdPEK3W8qCWON-FMSNXlbgyFPspv3Co3DWiukccK_2h8B38-liOStf39roZXA_6j2Q2hqBzNKElyQUOYp6yrXkyjjj0tRxIxlEuTLKhRfaMA9KOwkypEgs9d76kApaLUAkXcmOUbNYFHCCcIBok0oqvFUZN1orTnmAM8-FkQ5Se4raMRqT97X6xWQTiLM_5s_RXgz4uinwAjXL5Qou0a79LGcfy6tqz74BwDiXzw |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LTgIxFG0UTXSFD4xvu3BbmU7LtE3cGNBABDKJmLAjfSIJDgYHE__edhiQjQt3TTdNbtuce9t7zgHg1qfwzNhEICJdgmhCFBKWMqQJoTqRkcaKFmYTrN_nw6FIS7J6wYWx1hbNZ_YuDIu_fDPTi_BUVg_HxyOg2AY7DUpjvKRrreurILVSknRwJOqdtJW--PKnEfgocRAyjYKyyIaJSoEhT9V_rn4Aar9sPJiuceYQbNnsCFRXdgywvJ3H4H5DWxBK2AqSuMHNyhrUC_2033DgT1s2nlrYLB0i4MN0PJtP8rf3Gnh9ehw026g0R0CTOKI58mWOwA5TyalQRpk4NlRxYoNgGabMMamIs0IabrlPkkjsnHY-GdSSWRY1ODkBlWyW2VMAPUirmGPmtEioklJQTD2gOcoUNzbWZ6AWojH6WOpfjFaBOP9j_gbstQe97qjb6T9fgP0Q_GWL4CWo5POFvQK7-iuffM6vi_37AdcomxY |
| 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%3Abook&rft.genre=proceeding&rft.title=Proceedings+-+IEEE+International+Parallel+and+Distributed+Processing+Symposium&rft.atitle=Engineering+a+Distributed-Memory+Triangle+Counting+Algorithm&rft.au=Sanders%2C+Peter&rft.au=Uhl%2C+Tim+Niklas&rft.date=2023-05-01&rft.pub=IEEE&rft.eissn=1530-2075&rft.spage=702&rft.epage=712&rft_id=info:doi/10.1109%2FIPDPS54959.2023.00076&rft.externalDocID=10177419 |