An Improved/Optimized Practical Non-Blocking PageRank Algorithm for Massive Graphs
PageRank kernel is a standard benchmark addressing various graph processing and analytical problems. The PageRank algorithm serves as a standard for many graph analytics and a foundation for extracting graph features and predicting user ratings in recommendation systems. The PageRank algorithm is an...
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| Published in | International journal of parallel programming Vol. 50; no. 3-4; pp. 381 - 404 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.08.2022
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0885-7458 1573-7640 |
| DOI | 10.1007/s10766-022-00725-6 |
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| Abstract | PageRank kernel is a standard benchmark addressing various graph processing and analytical problems. The PageRank algorithm serves as a standard for many graph analytics and a foundation for extracting graph features and predicting user ratings in recommendation systems. The PageRank algorithm is an iterative algorithm that continuously updates the ranks of pages until it converges to a value. However, implementing the PageRank algorithm on a shared memory architecture while taking advantage of fine-grained parallelism with large-scale graphs is hard to implement. The experimental study and analysis of the parallel PageRank metric on large graphs and shared memory architectures using different programming models have been studied extensively. This paper presents the asynchronous execution of the PageRank algorithm to leverage the computations on massive graphs, especially on shared memory architectures. We evaluate the performance of our proposed non-blocking algorithms for PageRank computation on real-world and synthetic datasets using POSIX Multithreaded Library on a 56 core Intel(R) Xeon processor. We observed that our asynchronous implementations achieve
10
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to
30
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speed-up with respect to sequential runs and
5
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to
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improvements over synchronous variants. |
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| AbstractList | PageRank kernel is a standard benchmark addressing various graph processing and analytical problems. The PageRank algorithm serves as a standard for many graph analytics and a foundation for extracting graph features and predicting user ratings in recommendation systems. The PageRank algorithm is an iterative algorithm that continuously updates the ranks of pages until it converges to a value. However, implementing the PageRank algorithm on a shared memory architecture while taking advantage of fine-grained parallelism with large-scale graphs is hard to implement. The experimental study and analysis of the parallel PageRank metric on large graphs and shared memory architectures using different programming models have been studied extensively. This paper presents the asynchronous execution of the PageRank algorithm to leverage the computations on massive graphs, especially on shared memory architectures. We evaluate the performance of our proposed non-blocking algorithms for PageRank computation on real-world and synthetic datasets using POSIX Multithreaded Library on a 56 core Intel(R) Xeon processor. We observed that our asynchronous implementations achieve
10
×
to
30
×
speed-up with respect to sequential runs and
5
×
to
10
×
improvements over synchronous variants. PageRank kernel is a standard benchmark addressing various graph processing and analytical problems. The PageRank algorithm serves as a standard for many graph analytics and a foundation for extracting graph features and predicting user ratings in recommendation systems. The PageRank algorithm is an iterative algorithm that continuously updates the ranks of pages until it converges to a value. However, implementing the PageRank algorithm on a shared memory architecture while taking advantage of fine-grained parallelism with large-scale graphs is hard to implement. The experimental study and analysis of the parallel PageRank metric on large graphs and shared memory architectures using different programming models have been studied extensively. This paper presents the asynchronous execution of the PageRank algorithm to leverage the computations on massive graphs, especially on shared memory architectures. We evaluate the performance of our proposed non-blocking algorithms for PageRank computation on real-world and synthetic datasets using POSIX Multithreaded Library on a 56 core Intel(R) Xeon processor. We observed that our asynchronous implementations achieve 10× to 30× speed-up with respect to sequential runs and 5× to 10× improvements over synchronous variants. |
| Author | Eedi, Hemalatha Utkoor, Rahul Karra, Sahith Ranabothu, Neha Peri, Sathya |
| Author_xml | – sequence: 1 givenname: Hemalatha orcidid: 0000-0002-3717-1749 surname: Eedi fullname: Eedi, Hemalatha email: cs15resch11002@iith.ac.in organization: Indian Institute of Technology Hyderabad – sequence: 2 givenname: Sahith surname: Karra fullname: Karra, Sahith organization: American High School – sequence: 3 givenname: Sathya surname: Peri fullname: Peri, Sathya organization: Indian Institute of Technology Hyderabad – sequence: 4 givenname: Neha surname: Ranabothu fullname: Ranabothu, Neha organization: Indian Institute of Technology Hyderabad – sequence: 5 givenname: Rahul surname: Utkoor fullname: Utkoor, Rahul organization: Indian Institute of Technology Hyderabad |
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| Cites_doi | 10.1145/3243176.3243205 10.1137/1.9781611972740.43 10.1145/2150976.2151013 10.1109/IPDPS.2017.112 10.1145/2025113.2025133 10.1109/ICCD.2017.38 10.1609/aaai.v29i1.9277 10.1145/1807167.1807184 10.1145/3380942 10.1109/PDP52278.2021.00015 10.1145/2833312.2833322 10.1145/2517349.2522740 10.1016/j.future.2020.01.033 10.1145/2517349.2522739 10.1145/2442516.2442530 10.1145/2020976.2021006 10.1109/IPDPS.2009.5161102 10.1109/HiPC.2017.00013 |
| ContentType | Journal Article |
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| DOI | 10.1007/s10766-022-00725-6 |
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| Keywords | Multi Threading PageRank Non-blocking mechanism Barrier synchronization Blocking mechanism Shared memory architecture |
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| References_xml | – reference: Shun, J., Blelloch, G.E.: Ligra: a lightweight graph processing framework for shared memory. In: Nicolau, A., Shen, X., Amarasinghe, S.P., Vuduc, R.W. (eds) ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP ’13, Shenzhen, China, 23–27 February 2013, pp. 135–146. ACM (2013). https://doi.org/10.1145/2442516.2442530 – reference: Roy, A., Mihailovic, I., Zwaenepoel, W.: X-Stream: edge-centric graph processing using streaming partitions. In: Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, SOSP ’13, pp. 472–488. Association for Computing Machinery, New York (2013). https://doi.org/10.1145/2517349.2522740 – reference: Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection (2014). http://snap.stanford.edu/data – reference: Nguyen, D., Lenharth, A., Pingali, K.: A lightweight infrastructure for graph analytics. In: Kaminsky, M., Dahlin, M. (eds) ACM SIGOPS 24th Symposium on Operating Systems Principles, SOSP ’13, Farmington, PA, USA, 3–6 November 2013, pp. 456–471. ACM (2013). https://doi.org/10.1145/2517349.2522739 – reference: Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: AAAI (2015). http://networkrepository.com – reference: Low, Y., Gonzalez, J., Kyrola, A., Bickson, D., Guestrin, C., Hellerstein, J.M.: GraphLab: a new framework for parallel machine learning. In: Grünwald, P., Spirtes, P. (eds) UAI 2010, Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, Catalina Island, CA, USA, 8–11 July 2010, pp. 340–349. 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