A bridging model for parallel computation
Communications of the ACM
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the 20th international conference on World wide web
Green-Marl: a DSL for easy and efficient graph analysis
ASPLOS XVII Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Proceedings of the VLDB Endowment
Simplifying Scalable Graph Processing with a Domain-Specific Language
Proceedings of Annual IEEE/ACM International Symposium on Code Generation and Optimization
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Large-scale graph analysis has recently been drawing lots of attention from both industry and academia. Although there are already several frameworks designed for scalable graph analysis, e.g. Giraph [1], all these frameworks adopt non-traditional programming models and APIs. This can significantly lower the productivity of the framework user. This paper discusses the feasibility of using an intuitive Domain-Specific Language (DSL) for graph analysis. Specifically, we use a compiler to translate Green-Marl [5] programs into an equivalent Giraph application, automatically bridging between very different programming models. We observe that the DSL programs are concise and intuitive, and that the compiler generated Giraph implementations exhibit performance on par with that of hand-written ones. However, the DSL compilation cannot but fail if the algorithm is fundamentally not compatible with the target framework. Overall, we believe that the DSL-based approach will provide great productivity benefits once it matures.