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Tencent Open-Sources High-Performance Graph Computing Framework ‘Plato’

Last week, Tencent Graph Computing (TGraph) officially announced the release of Plato, an open source high-performance graph computing framework that meets the ultra-large-scale graph computing requirement of billion-level nodes. Plato can shorten algorithm computing time from days to just minutes and reduce the number of servers required to complete a task from hundreds to only about ten — feats unattainable by any other mainstream distributed graph computing framework.

Graph computing combines data from different sources and of different kinds into the same graph to find correlations and connections which are difficult to discern through distinct data analysis approaches. Graph computing is increasingly used as a data analysis and mining tool across social networks and recommendation systems, as well as in the cyber security, text retrieval and biomedical fields.

The main contributions of the Plato framework are:

Comparisons between Plato and Spark GraphX on representative benchmarks (left shows time required for calculation, right shows memory consumption).

Plato can provide efficient offline graph computing and graph representation learning for social network data on the massive scale produced by Tencent. It runs on general X86 clusters, such as Kubernetes and Yarn clusters, and supports multiple interfaces for mainstream file systems such as HDFS and Ceph.

At the Tech Echo Developer Conference earlier this month Tencent also officially announced the open-sourcing of four other key projects: TubeMQ, Tencent Kona JDK, TBase, and TKEStack. So far Tencent has open sourced 86 projects on GitHub, ranking among the leading software-sharing platform’s top 10 global corporate contributors.

The Plato Graph Computing Framework, installation instructions and further information is available on GitHub.


Author: Reina Qi Wan | Editor: Michael Sarazen

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