We present here the results of our investigation of a transactional model of parallel programming on cluster computing systems. This model is specifically targeted for graph applications with the goal of harnessing unstructured parallelism inherently present in many such problems. In this model, tasks for vertex-centric computations are executed optimistically in parallel as serializable transactions. A key-value based globally shared object store is implemented in the main memory of the cluster nodes for storing the graph data. Task computations read and modify data in the distributed global store, without any explicitly programmed message-passing in the application code. Based on this model we developed a framework for parallel programming of graph applications on computing clusters. We present here the programming abstractions provided by this framework and its architecture. Using several graph problems we illustrate the simplicity of the abstractions provided by this model. These problems include graph coloring, k-nearest neighbors, and single-source shortest path computation. We also illustrate how incremental computations can be supported by this programming model. Using these problems we evaluate the transactional programming model and the mechanisms provided by this framework.
|Original language||English (US)|
|Title of host publication||Proceedings - 2017 IEEE 10th International Conference on Cloud Computing, CLOUD 2017|
|Editors||Geoffrey C. Fox|
|Publisher||IEEE Computer Society|
|Number of pages||8|
|State||Published - Sep 8 2017|
|Event||10th IEEE International Conference on Cloud Computing, CLOUD 2017 - Honolulu, United States|
Duration: Jun 25 2017 → Jun 30 2017
|Name||IEEE International Conference on Cloud Computing, CLOUD|
|Other||10th IEEE International Conference on Cloud Computing, CLOUD 2017|
|Period||6/25/17 → 6/30/17|
Bibliographical noteFunding Information:
Acknowledgements: This work was supported by NSF Award 1319333 and computing resources were provided by NSF award 1512877 and the Minnesota Supercomputing Institute.
© 2017 IEEE.
Copyright 2017 Elsevier B.V., All rights reserved.
- Cluster computing
- Concurrency control
- Distributed Systems
- Graph problems
- Parallel computing
- Transaction models