Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2019
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| _version_ | 1866913619509248000 |
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| author | Das, Anindya Bijoy Ramamoorthy, Aditya |
| author_facet | Das, Anindya Bijoy Ramamoorthy, Aditya |
| contents | Distributed computing systems are well-known to suffer from the problem of slow or failed nodes; these are referred to as stragglers. Straggler mitigation (for distributed matrix computations) has recently been investigated from the standpoint of erasure coding in several works. In this work we present a strategy for distributed matrix-vector multiplication based on convolutional coding. Our scheme can be decoded using a low-complexity peeling decoder. The recovery process enjoys excellent numerical stability as compared to Reed-Solomon coding based approaches (which exhibit significant problems owing their badly conditioned decoding matrices). Finally, our schemes are better matched to the practically important case of sparse matrix-vector multiplication as compared to many previous schemes. Extensive simulation results corroborate our findings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1901_08716 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach Das, Anindya Bijoy Ramamoorthy, Aditya Information Theory Distributed, Parallel, and Cluster Computing Numerical Analysis Distributed computing systems are well-known to suffer from the problem of slow or failed nodes; these are referred to as stragglers. Straggler mitigation (for distributed matrix computations) has recently been investigated from the standpoint of erasure coding in several works. In this work we present a strategy for distributed matrix-vector multiplication based on convolutional coding. Our scheme can be decoded using a low-complexity peeling decoder. The recovery process enjoys excellent numerical stability as compared to Reed-Solomon coding based approaches (which exhibit significant problems owing their badly conditioned decoding matrices). Finally, our schemes are better matched to the practically important case of sparse matrix-vector multiplication as compared to many previous schemes. Extensive simulation results corroborate our findings. |
| title | Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach |
| topic | Information Theory Distributed, Parallel, and Cluster Computing Numerical Analysis |
| url | https://arxiv.org/abs/1901.08716 |