Balanced parallel triangle enumeration with an adaptive algorithm
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| Publicado en: | Distributed and Parallel Databases vol. 42, no. 1 (Mar 2024), p. 103 |
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| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1007/s10619-023-07437-x |2 doi | |
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| 045 | 2 | |b d20240301 |b d20240331 | |
| 100 | 1 | |a Farouzi, Abir |u ISAE-ENSMA, Poitiers, France (GRID:grid.434217.7) (ISNI:0000 0001 2178 9782); Ecole Supérieure en Informatique, Sidi Bel Abbès, Algeria (GRID:grid.434217.7) | |
| 245 | 1 | |a Balanced parallel triangle enumeration with an adaptive algorithm | |
| 260 | |b Springer Nature B.V. |c Mar 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Triangle enumeration is a foundation brick for solving harder graph problems related to social networks, the Internet and transportation, to name a few applications. This problem is well studied in the theory literature, but remains an open problem with big data. In this paper, we defend the idea of solving triangle enumeration with SQL queries evaluating the steps of a new adaptive algorithm with linear speedup. Such SQL approach provides scalability beyond RAM limits, automatic parallel processing and more importantly: linear speedup as more machines are added. We present theory results and experimental validation showing our solution works well with large graphs analyzed on a parallel cluster with many machines, producing a balanced workload even with highly skewed degree vertices. We consider two types of distributed systems: (1) a parallel DBMS that evaluates SQL queries, and (2) a parallel HPC cluster calling the MPI library (called via Python). Extensive benchmark experiments with large graphs show our SQL solution offers many advantages over MPI and competing graph analytic systems. | |
| 653 | |a Parallel processing | ||
| 653 | |a Big Data | ||
| 653 | |a Graphs | ||
| 653 | |a Apexes | ||
| 653 | |a Infrastructure | ||
| 653 | |a Social networks | ||
| 653 | |a Queries | ||
| 653 | |a Graph representations | ||
| 653 | |a Optimization | ||
| 653 | |a Data processing | ||
| 653 | |a Architecture | ||
| 653 | |a Enumeration | ||
| 653 | |a Algorithms | ||
| 653 | |a Clusters | ||
| 653 | |a Query languages | ||
| 653 | |a Adaptive algorithms | ||
| 700 | 1 | |a Zhou, Xiantian |u University of Houston, Houston, USA (GRID:grid.266436.3) (ISNI:0000 0004 1569 9707) | |
| 700 | 1 | |a Bellatreche, Ladjel |u ISAE-ENSMA, Poitiers, France (GRID:grid.434217.7) (ISNI:0000 0001 2178 9782) | |
| 700 | 1 | |a Malki, Mimoun |u Ecole Supérieure en Informatique, Sidi Bel Abbès, Algeria (GRID:grid.434217.7) | |
| 700 | 1 | |a Ordonez, Carlos |u University of Houston, Houston, USA (GRID:grid.266436.3) (ISNI:0000 0004 1569 9707) | |
| 773 | 0 | |t Distributed and Parallel Databases |g vol. 42, no. 1 (Mar 2024), p. 103 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3255419737/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3255419737/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3255419737/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |