Abstract
The efficiency of matrix multiplication is a popular research topic given that matrices compromise large data in computer applications and other fields of study. The proposed schemes utilize data blocks to balance processing overhead results from a small mapper set and I/O overhead results from a large mapper set. Balancing between the two processing steps, however, consumes time and resources. The proposed technique uses a single MapReduce job and pre-processing step. The pre-processing step reads an element from the first array and a block from the second array prior to merging both elements into one file. The map task performs the multiplication operations, whereas the reduce task performs the sum operations. Comparing the proposed and existing schemes reveals that the proposed schemes more efficiently consume time and memory.
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Kadhum, M., Qasem, M.H., Sleit, A., Sharieh, A. (2017). Efficient MapReduce Matrix Multiplication with Optimized Mapper Set. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Cybernetics and Mathematics Applications in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 574. Springer, Cham. https://doi.org/10.1007/978-3-319-57264-2_19
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