Difference between revisions of "Mirhoseini2016perform"
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|keywords=Machine Learning | |keywords=Machine Learning | ||
|abstract=<p>We propose Perform-ML, the first Machine Learning (ML) framework for analysis of massive and dense data which customizes the algorithm to the underlying platform for the purpose of achieving optimized resource efficiency. PerformML creates a performance model quantifying the computational cost of iterative analysis algorithms on a pertinent platform in terms of FLOPs, communication, and memory, which characterize runtime, storage, and energy. The core of Perform-ML is a novel parametric data projection algorithm, called Elastic Dictionary (ExD), that enables versatile and sparse representations of the data which can help in minimizing performance cost. We show that Perform-ML can achieve the optimal performance objective, according to our cost model, by platform-aware tuning of the ExD parameters. An accompanying API ensures automated applicability of Perform-ML to various algorithms, datasets, and platforms. Proof-of-concept evaluations of massive and dense data on different platforms demonstrate more than an order of magnitude improvements in performance compared to the state-of-the-art, within guaranteed user-defined error bounds.</p> | |abstract=<p>We propose Perform-ML, the first Machine Learning (ML) framework for analysis of massive and dense data which customizes the algorithm to the underlying platform for the purpose of achieving optimized resource efficiency. PerformML creates a performance model quantifying the computational cost of iterative analysis algorithms on a pertinent platform in terms of FLOPs, communication, and memory, which characterize runtime, storage, and energy. The core of Perform-ML is a novel parametric data projection algorithm, called Elastic Dictionary (ExD), that enables versatile and sparse representations of the data which can help in minimizing performance cost. We show that Perform-ML can achieve the optimal performance objective, according to our cost model, by platform-aware tuning of the ExD parameters. An accompanying API ensures automated applicability of Perform-ML to various algorithms, datasets, and platforms. Proof-of-concept evaluations of massive and dense data on different platforms demonstrate more than an order of magnitude improvements in performance compared to the state-of-the-art, within guaranteed user-defined error bounds.</p> | ||
|month=6 | |||
|year=2016 | |||
|booktitle=Design Automation Conference (DAC) | |booktitle=Design Automation Conference (DAC) | ||
|title=Perform-ML: Performance Optimized Machine Learning by Platform and Content Aware Customization | |title=Perform-ML: Performance Optimized Machine Learning by Platform and Content Aware Customization | ||
|entry=inproceedings | |entry=inproceedings | ||
| | |pdf=Mirhoseini2016perform.pdf | ||
}} | }} | ||
Latest revision as of 17:38, 9 November 2021
| Mirhoseini2016perform | |
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| entry | inproceedings |
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| author | Azalia Mirhoseini and Bita Darvish Rouhani and Songhori, Ebrahim M. and Farinaz Koushanfar |
| booktitle | Design Automation Conference (DAC) |
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| month | 6 |
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| title | Perform-ML: Performance Optimized Machine Learning by Platform and Content Aware Customization |
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| year | 2016 |
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| url | dl.acm.org/citation.cfm?id=2898060 |
| Mirhoseini2016perform.pdf | |
Email:
farinaz@ucsd.edu
farinaz@ucsd.edu
Address:
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Lab Location: EBU1-2514
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093