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- W3136257861 abstract "Strassen’s block-recursive matrix multiplication is amenable to parallelization via distributed recursion. Recently, distributed implementations of Strassen’s algorithm using Big-data frameworks, e.g. Apache Spark have emerged for matrices of orders which are powers of 2. This paper studies an imple-mentation of distributed block-recursive matrix multiplication algorithm for matrices of arbitrary order with minimal zero padding. The conducted experiments show that our implementation has strong scalability with increasing matrix size enabling us to multiply large matrices with upto 21% less wall clock time than MLLib, the in-built matrix multiplication implementation in Spark. We report an interesting pattern in optimal block-size as a function of matrix size." @default.
- W3136257861 created "2021-03-29" @default.
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- W3136257861 date "2020-12-10" @default.
- W3136257861 modified "2023-10-16" @default.
- W3136257861 title "An Optimized Distributed Recursive Matrix Multiplication for Arbitrary Sized Matrices" @default.
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- W3136257861 doi "https://doi.org/10.1109/bigdata50022.2020.9378361" @default.
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