
Cuboid Partitioning for Hierarchical Coded Matrix Multiplication
Coded matrix multiplication is a technique to enable stragglerresistant...
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Stark: Fast and Scalable Strassen's Matrix Multiplication using Apache Spark
This paper presents a new fast, highly scalable distributed matrix multi...
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OverSketch: Approximate Matrix Multiplication for the Cloud
We propose OverSketch, an approximate algorithm for distributed matrix m...
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Localized sketching for matrix multiplication and ridge regression
We consider sketched approximate matrix multiplication and ridge regress...
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ParityChecked Strassen Algorithm
To multiply astronomic matrices using parallel workers subject to stragg...
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Matrix Multiplication and Binary Space Partitioning Trees : An Exploration
Herein we explore a dual tree algorithm for matrix multiplication of A∈ℝ...
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Impossibility Results for GrammarCompressed Linear Algebra
To handle vast amounts of data, it is natural and popular to compress ve...
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Approximate Weighted CR Coded Matrix Multiplication
One of the most common, but at the same time expensive operations in linear algebra, is multiplying two matrices A and B. With the rapid development of machine learning and increases in data volume, performing fast matrix intensive multiplications has become a major hurdle. Two different approaches to overcoming this issue are, 1) to approximate the product; and 2) to perform the multiplication distributively. A CRmultiplication is an approximation where columns and rows of A and B are respectively sampled with replacement. In the distributed setting, multiple workers perform matrix multiplication subtasks in parallel. Some of the workers may be stragglers, meaning they do not complete their task in time. We present a novel approximate weighted CR coded matrix multiplication scheme, that achieves improved performance for distributed matrix multiplication.
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