A bridging model for parallel computation
Communications of the ACM
Introduction to algorithms
A parallel algorithm for multilevel graph partitioning and sparse matrix ordering
Journal of Parallel and Distributed Computing
Introduction to Distributed Algorithms
Introduction to Distributed Algorithms
The Case for High-Level Parallel Programming in ZPL
IEEE Computational Science & Engineering
On Identifying Strongly Connected Components in Parallel
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
An evaluation of global address space languages: co-array fortran and unified parallel C
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Lifting sequential graph algorithms for distributed-memory parallel computation
OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
X10: an object-oriented approach to non-uniform cluster computing
OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
ACM SIGIR Forum
Improved Distributed Algorithms for SCC Decomposition
Electronic Notes in Theoretical Computer Science (ENTCS)
Solving Large, Irregular Graph Problems Using Adaptive Work-Stealing
ICPP '08 Proceedings of the 2008 37th International Conference on Parallel Processing
"To store or not to store" reloaded: reclaiming memory on demand
FMICS'06/PDMC'06 Proceedings of the 11th international workshop, FMICS 2006 and 5th international workshop, PDMC conference on Formal methods: Applications and technology
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
HipG: parallel processing of large-scale graphs
ACM SIGOPS Operating Systems Review
Using Pregel-like Large Scale Graph Processing Frameworks for Social Network Analysis
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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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.