Title
Green-Marl: a DSL for easy and efficient graph analysis
Abstract
The increasing importance of graph-data based applications is fueling the need for highly efficient and parallel implementations of graph analysis software. In this paper we describe Green-Marl, a domain-specific language (DSL) whose high level language constructs allow developers to describe their graph analysis algorithms intuitively, but expose the data-level parallelism inherent in the algorithms. We also present our Green-Marl compiler which translates high-level algorithmic description written in Green-Marl into an efficient C++ implementation by exploiting this exposed data-level parallelism. Furthermore, our Green-Marl compiler applies a set of optimizations that take advantage of the high-level semantic knowledge encoded in the Green-Marl DSL. We demonstrate that graph analysis algorithms can be written very intuitively with Green-Marl through some examples, and our experimental results show that the compiler-generated implementation out of such descriptions performs as well as or better than highly-tuned hand-coded implementations.
Year
DOI
Venue
2012
10.1145/2150976.2151013
ASPLOS
Keywords
Field
DocType
high level language construct,compiler-generated implementation,domain-specific language,green-marl compiler,efficient graph analysis,graph analysis algorithms intuitively,exposed data-level parallelism,green-marl dsl,graph analysis algorithm,graph analysis software,efficient c,graph,high level language,data level parallelism,parallel programming,domain specific language
Domain-specific language,Programming language,Implicit parallelism,Computer science,Digital subscriber line,Parallel computing,Implementation,Compiler,Power graph analysis,Theoretical computer science,Software,High-level programming language
Conference
Volume
Issue
ISSN
40
1
0163-5964
Citations 
PageRank 
References 
119
4.03
19
Authors
4
Search Limit
100119
Name
Order
Citations
PageRank
Sungpack Hong186433.20
Hassan Chafi2111861.11
Edic Sedlar31194.03
Kunle Olukotun44532373.50