SciHadoop: array-based query processing in Hadoop
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
MadLINQ: large-scale distributed matrix computation for the cloud
Proceedings of the 7th ACM european conference on Computer Systems
Matrix chain multiplication via multi-way join algorithms in MapReduce
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
A flexible parallel runtime for large scale block-based matrix multiplication
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
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Cumulon: optimizing statistical data analysis in the cloud
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Mizan: a system for dynamic load balancing in large-scale graph processing
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Presto: distributed machine learning and graph processing with sparse matrices
Proceedings of the 8th ACM European Conference on Computer Systems
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First International Workshop on Graph Data Management Experiences and Systems
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Performance comparison under failures of MPI and MapReduce: An analytical approach
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Frontiers of Computer Science: Selected Publications from Chinese Universities
Efficient query evaluation on distributed graphs with Hadoop environment
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Parallel processing of large graphs
Future Generation Computer Systems
Exploiting inter-operation parallelism for matrix chain multiplication using MapReduce
The Journal of Supercomputing
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Various scientific computations have become so complex, and thus computation tools play an important role. In this paper, we explore the state-of-the-art framework providing high-level matrix computation primitives with MapReduce through the case study approach, and demonstrate these primitives with different computation engines to show the performance and scalability. We believe the opportunity for using MapReduce in scientific computation is even more promising than the success to date in the parallel systems literature.