Projects per year
This paper proposes an Adaptive Asynchronous Parallel (AAP) model for graph computations. As opposed to Bulk Synchronous Parallel (BSP) and Asynchronous Parallel (AP) models, AAP reduces both stragglers and stale computations by dynamically adjusting relative progress of workers. We show that BSP, AP and Stale Synchronous Parallel model (SSP) are special cases of AAP. Better yet, AAP optimizes parallel processing by adaptively switching among these models at different stages of a single execution. Moreover, employing the programming model of GRAPE, AAP aims to parallelize existing sequential algorithms based on fixpoint computation with partial and incremental evaluation. Under a monotone condition, AAP guarantees to converge at correct answers if the sequential algorithms are correct. Furthermore, we show that AAP can optimally simulate MapReduce, PRAM, BSP, AP and SSP. Using real-life and synthetic graphs, we experimentally verify that AAP outperforms BSP, AP and SSP for a variety of graph computations.
|Title of host publication||Proceedings of the 2018 International Conference on Management of Data (SIGMOD'18)|
|Place of Publication||Texas, USA|
|Number of pages||16|
|Publication status||Published - 27 May 2018|
|Event||2018 ACM SIGMOD/PODS International Conference on Management of Data - Houston, United States|
Duration: 10 Jun 2018 → 15 Jun 2018
|Name||International Conference on Management of Data|
|Conference||2018 ACM SIGMOD/PODS International Conference on Management of Data|
|Period||10/06/18 → 15/06/18|
- parallel model
- graph computations
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- 2 Finished
GRACE-Resource Bounded Graph Query Answering
1/11/15 → 31/10/21
VADA: Value Added Data Systems: Principles and Architecture
Libkin, L., Buneman, P., Fan, W. & Pieris, A.
1/04/15 → 30/09/20
- School of Informatics - Personal Chair in Web Data Management
- Laboratory for Foundations of Computer Science
- Foundations of Computation
Person: Academic: Research Active