Distributed processing of real-world graphs is challenging due to their size and the inherent irregular structure of graph computations. We present hipg, a distributed framework that facilitates high-level programming of parallel graph algorithms by expressing them as a hierarchy of distributed computations executed independently and managed by the user. hipg programs are in general short and elegant; they achieve good portability, memory utilization and performance
© 1989-2012 IEEE. Hypergraphs are generalizations of graphs where the (hyper)edges can connect any n...
Abstract—Graph algorithms on distributed-memory systems typically perform heavy communication, often...
Implementing machine learning algorithms for large data, such as the Web graph and social networks, ...
Distributed processing of real-world graphs is challenging due to their size and the inherent irregu...
Distributed processing of real-world graphs is challenging due to their size and the inherent irregu...
The world is becoming a more conjunct place and the number of data sources such as social networks, ...
The world is becoming a more conjunct place and the number of data sources such as social networks, ...
Graph models of social information systems typically contain trillions of edges. Such big graphs can...
Graph processing is increasingly popular in a variety of scientific and engineering domains. Consequ...
Abstract. This paper describes the stapl Parallel Graph Library, a high-level framework that abstrac...
In recent years, processing and analysing large graphs has become a major need in many research area...
Efficiently processing large graphs is challenging, since parallel graph algorithms suffer from poor...
The last decade has seen an increased attention on large-scale data analysis, caused mainly by the a...
Graphs have become increasingly important to represent highly-interconnected structures and schema-l...
There has been significant recent interest in parallel graph processing due to the need to quickly a...
© 1989-2012 IEEE. Hypergraphs are generalizations of graphs where the (hyper)edges can connect any n...
Abstract—Graph algorithms on distributed-memory systems typically perform heavy communication, often...
Implementing machine learning algorithms for large data, such as the Web graph and social networks, ...
Distributed processing of real-world graphs is challenging due to their size and the inherent irregu...
Distributed processing of real-world graphs is challenging due to their size and the inherent irregu...
The world is becoming a more conjunct place and the number of data sources such as social networks, ...
The world is becoming a more conjunct place and the number of data sources such as social networks, ...
Graph models of social information systems typically contain trillions of edges. Such big graphs can...
Graph processing is increasingly popular in a variety of scientific and engineering domains. Consequ...
Abstract. This paper describes the stapl Parallel Graph Library, a high-level framework that abstrac...
In recent years, processing and analysing large graphs has become a major need in many research area...
Efficiently processing large graphs is challenging, since parallel graph algorithms suffer from poor...
The last decade has seen an increased attention on large-scale data analysis, caused mainly by the a...
Graphs have become increasingly important to represent highly-interconnected structures and schema-l...
There has been significant recent interest in parallel graph processing due to the need to quickly a...
© 1989-2012 IEEE. Hypergraphs are generalizations of graphs where the (hyper)edges can connect any n...
Abstract—Graph algorithms on distributed-memory systems typically perform heavy communication, often...
Implementing machine learning algorithms for large data, such as the Web graph and social networks, ...