Fast, General Parallel Computation for Machine Learning
| Autor: | Norman S. Matloff, Robin Elizabeth Yancey |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science Big data 020206 networking & telecommunications Context (language use) 02 engineering and technology Parallel computing Recommender system Machine learning computer.software_genre 01 natural sciences 010104 statistics & probability Software Factor (programming language) 0202 electrical engineering electronic engineering information engineering Artificial intelligence 0101 mathematics business computer computer.programming_language |
| Zdroj: | ICPP Workshops |
| DOI: | 10.1145/3229710.3229716 |
| Popis: | Today the terms machine learning (ML) and Big Data are closely correlated. This, and the complexity of many ML algorithms, motivates a search for fast parallel computation methods. A further motivating factor is a need to deal with memory size limitations, especially for the moderately-sized machines common in many ML applications. In addition, it is desirable to develop generally applicable methods, rather than needing to develop a different parallel approach for every ML algorithm. In this work, we apply a technique we call Software Alchemy to ML. We are particularly interested in ML for recommender systems, and explore the feasibility of SA in that context. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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