Abstract
We demonstrate GRAPE, a parallel GRAPh query Engine. GRAPE advocates a parallel model based on a simultaneous fixed point computation in terms of partial and incremental evaluation. It differs from prior systems in its ability to parallelize existing sequential graph algorithms as a whole, without the need for recasting the entire algorithms into a new model. One of its unique features is that under a monotonic condition, GRAPE parallelization guarantees to terminate with correct answers as long as the sequential algorithms "plugged in" are correct. We demonstrate its parallel computations, ease-of-use and performance compared with the start-of-the-art graph systems. We also demonstrate a use case of GRAPE in social media marketing.
Original language | English |
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Pages (from-to) | 1889-1892 |
Number of pages | 4 |
Journal | Proceedings of the VLDB Endowment |
Volume | 10 |
Issue number | 12 |
DOIs | |
Publication status | Published - 1 Aug 2017 |
Event | 43rd International Conference on Very Large Data Bases, VLDB 2017 - Munich, Germany Duration: 28 Aug 2017 → 1 Sept 2017 |