Abstract | ||
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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 |
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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 |
Name | Order | Citations | PageRank |
---|---|---|---|
Sungpack Hong | 1 | 864 | 33.20 |
Hassan Chafi | 2 | 1118 | 61.11 |
Edic Sedlar | 3 | 119 | 4.03 |
Kunle Olukotun | 4 | 4532 | 373.50 |