Green-Marl: a DSL for easy and efficient graph analysis

By Sungpack Hong, Hassan Chafi, Eric Sedlar, Kunle Olukotun. ASPLOS XVII, 2012

328 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/e82ebc17c5eace74f5e300dce8fff69b1d5118e9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Hardware and Architecture

Citations in the corpus

A lightweight infrastructure for graph analytics (2013, methodology): “Several domain-specific languages (DSLs) for graph analytics have been proposed recently for simplifying the task of writing these programs [11, 12, 17–19, 31].”
One Trillion Edges: Graph Processing at Facebook-Scale (2015, methodology): “Several DSL graph languages have been built, including Green-Marl [25], which can compile to Giraph code, and SPARQL [26], a graph traversal language.”
PIM-enabled instructions: A low-overhead, locality-aware processing-in-memory architecture (2015, background): “Section 4 will describe our detailed implementation to realize this abstraction.”
EmptyHeaded: A Relational Engine for Graph Processing (2015, result): “H.2 [Information Systems]: Database Management System Engines”
Ligra: a lightweight graph processing framework for shared memory (2013, background): “Green-Marl is a domain-specific language for writing graph algorithms for shared-memory [21].”
Gunrock: a high-performance graph processing library on the GPU (2015, background): “Green-Marl [15] is a domain-specific language for writing graph analysis algorithms on shared memory with built-in breadth-first search (BFS) and depth-first search (DFS) primitives in its compiler.”
Foundations of Modern Query Languages for Graph Databases (2016, background): “Furthermore, there has recently been a lot of work on domain-specific languages that can take care of particular sets of operations within a certain domain or scenario (see, e.g., Hong et al. (2012)).”
Processing Data Where It Makes Sense: Enabling In-Memory Computation (2019, background): “A popular modern application is large-scale graph processing [87, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193].”
A scalable processing-in-memory accelerator for parallel graph processing (2015, methodology): “Our comprehensive evaluations using five state-of-the-art graph processing workloads with large real-world graphs show that the proposed architecture improves average system performance by a factor of ten and achieves 87% average energy reduction over conventional systems.”
GraphMat: High performance graph analytics made productive (2015, background): “Other domain specific languages such as GreenMarl [16] purport to improve productivity and performance, but at the cost of a having to learn a new programming language.”
Graphicionado: A high-performance and energy-efficient accelerator for graph analytics (2016, background): “Examples include vertex programming [4, 33, 35, 46], sparse matrix operations [8], graph domain-specific languages [18], and task-based models [23].”