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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2110.03423 |
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| _version_ | 1866909134181367808 |
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| author | Struski, Łukasz Morkisz, Paweł Spurek, Przemysław Bernabeu, Samuel Rodriguez Trzciński, Tomasz |
| author_facet | Struski, Łukasz Morkisz, Paweł Spurek, Przemysław Bernabeu, Samuel Rodriguez Trzciński, Tomasz |
| contents | Matrix decompositions are ubiquitous in machine learning, including applications in dimensionality reduction, data compression and deep learning algorithms. Typical solutions for matrix decompositions have polynomial complexity which significantly increases their computational cost and time. In this work, we leverage efficient processing operations that can be run in parallel on modern Graphical Processing Units (GPUs), predominant computing architecture used e.g. in deep learning, to reduce the computational burden of computing matrix decompositions. More specifically, we reformulate the randomized decomposition problem to incorporate fast matrix multiplication operations (BLAS-3) as building blocks. We show that this formulation, combined with fast random number generators, allows to fully exploit the potential of parallel processing implemented in GPUs. Our extensive evaluation confirms the superiority of this approach over the competing methods and we release the results of this research as a part of the official CUDA implementation (https://docs.nvidia.com/cuda/cusolver/index.html). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2110_03423 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Efficient GPU implementation of randomized SVD and its applications Struski, Łukasz Morkisz, Paweł Spurek, Przemysław Bernabeu, Samuel Rodriguez Trzciński, Tomasz Machine Learning Performance Matrix decompositions are ubiquitous in machine learning, including applications in dimensionality reduction, data compression and deep learning algorithms. Typical solutions for matrix decompositions have polynomial complexity which significantly increases their computational cost and time. In this work, we leverage efficient processing operations that can be run in parallel on modern Graphical Processing Units (GPUs), predominant computing architecture used e.g. in deep learning, to reduce the computational burden of computing matrix decompositions. More specifically, we reformulate the randomized decomposition problem to incorporate fast matrix multiplication operations (BLAS-3) as building blocks. We show that this formulation, combined with fast random number generators, allows to fully exploit the potential of parallel processing implemented in GPUs. Our extensive evaluation confirms the superiority of this approach over the competing methods and we release the results of this research as a part of the official CUDA implementation (https://docs.nvidia.com/cuda/cusolver/index.html). |
| title | Efficient GPU implementation of randomized SVD and its applications |
| topic | Machine Learning Performance |
| url | https://arxiv.org/abs/2110.03423 |