Paper Info
Title
Simplifying Scalable Graph Processing with a Domain-Specific Language
Abstract
Large-scale graph processing, with its massive data sets, requires distributed processing. However, conventional frameworks for distributed graph processing, such as Pregel, use non-traditional programming models that are well-suited for parallelism and scalability but inconvenient for implementing non-trivial graph algorithms. In this paper, we use Green-Marl, a Domain-Specific Language for graph analysis, to intuitively describe graph algorithms and extend its compiler to generate equivalent Pregel implementations. Using the semantic information captured by Green-Marl, the compiler applies a set of transformation rules that convert imperative graph algorithms into Pregel's programming model. Our experiments show that the Pregel programs generated by the Green-Marl compiler perform similarly to manually coded Pregel implementations of the same algorithms. The compiler is even able to generate a Pregel implementation of a complicated graph algorithm for which a manual Pregel implementation is very challenging.
Year
DOI
Venue
2014
10.1145/2544137.2544162
CGO
Keywords
Field
DocType
equivalent pregel implementation,pregel implementation,domain-specific language,graph analysis,graph processing,imperative graph algorithm,manual pregel implementation,pregel program,simplifying scalable graph processing,complicated graph algorithm,graph algorithm,large-scale graph processing
Domain-specific language,Graph,Data set,Programming language,Programming paradigm,Computer science,Parallel computing,Power graph analysis,Compiler,Implementation,Scalability
Conference
ISBN
Citations 
PageRank 
978-1-4503-2670-4
16
0.65
References 
Authors
14
4
Name
Order
Citations
PageRank
Sungpack Hong186433.20
Semih Salihoglu243324.83
Jennifer Widom3161502524.75
Kunle Olukotun44532373.50