Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Path-hop: efficiently indexing large graphs for reachability queries
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Cloud-based Connected Component Algorithm
AICI '10 Proceedings of the 2010 International Conference on Artificial Intelligence and Computational Intelligence - Volume 03
On triangulation-based dense neighborhood graph discovery
Proceedings of the VLDB Endowment
Ex-MATE: Data Intensive Computing with Large Reduction Objects and Its Application to Graph Mining
CCGRID '11 Proceedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Proceedings of the VLDB Endowment
Hadoop: The Definitive Guide
Spinning fast iterative data flows
Proceedings of the VLDB Endowment
Optimizing Large-scale Graph Analysis on Multithreaded, Multicore Platforms
IPDPS '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium
Ligra: a lightweight graph processing framework for shared memory
Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming
Trinity: a distributed graph engine on a memory cloud
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
XGDBench: A benchmarking platform for graph stores in exascale clouds
CLOUDCOM '12 Proceedings of the 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom)
Resource Management for Dynamic MapReduce Clusters in Multicluster Systems
SCC '12 Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis
Proceedings of the 11th Annual Workshop on Network and Systems Support for Games
Minimizing Communication in All-Pairs Shortest Paths
IPDPS '13 Proceedings of the 2013 IEEE 27th International Symposium on Parallel and Distributed Processing
High-Productivity and High-Performance Analysis of Filtered Semantic Graphs
IPDPS '13 Proceedings of the 2013 IEEE 27th International Symposium on Parallel and Distributed Processing
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Processing graphs, especially at large scale, is an increasingly useful activity in a variety of business, engineering, and scientific domains. Already, there are tens of graph-processing platforms, such as Hadoop, Giraph, GraphLab, etc., each with a different design and functionality. For graph-processing to continue to evolve, users have to find it easy to select a graph-processing platform, and developers and system integrators have to find it easy to quantify the performance and other non-functional aspects of interest. However, the state of performance analysis of graph-processing platforms is still immature: there are few studies and, for the few that exist, there are few similarities, and relatively little understanding of the impact of dataset and algorithm diversity on performance. Our vision is to develop, with the help of the performance-savvy community, a comprehensive benchmarking suite for graph-processing platforms. In this work, we take a step in this direction, by proposing a set of seven challenges, summarizing our previous work on performance evaluation of distributed graph-processing platforms, and introducing our on-going work within the SPEC Research Group's Cloud Working Group.