A bridging model for parallel computation
Communications of the ACM
Two-level pipelined systolic arrays for matrix-vector multiplication
Journal of Systems Architecture: the EUROMICRO Journal
Introduction to algorithms
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Faster join-projects and sparse matrix multiplications
Proceedings of the 12th International Conference on Database Theory
PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Hive: a warehousing solution over a map-reduce framework
Proceedings of the VLDB Endowment
SPARQL basic graph pattern processing with iterative MapReduce
Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A comparison of join algorithms for log processing in MaPreduce
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
HAMA: An Efficient Matrix Computation with the MapReduce Framework
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
SystemML: Declarative machine learning on MapReduce
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Optimizing Multiway Joins in a Map-Reduce Environment
IEEE Transactions on Knowledge and Data Engineering
Matrix chain multiplication via multi-way join algorithms in MapReduce
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
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In this paper, we address the matrix chain multiplication problem, i.e., the multiplication of several matrices. Although several studies have investigated the problem, our approach has some different points. First, we propose MapReduce algorithms that allow us to provide scalable computation for large matrices. Second, we transform the matrix chain multiplication problem from sequential multiplications of two matrices into a single multiplication of several matrices. Since matrix multiplication is associative, this approach helps to improve the performance of the algorithms. To implement the idea, we adopt multi-way join algorithms in MapReduce that have been studied in recent years. In our experiments, we show that the proposed algorithms are fast and scalable, compared to several baseline algorithms.