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About Distributed Graphlab: A Framework For Machine Learning and Data Mining in The Cloud (12) Kashif Rabbani
The document summarizes the GraphLab framework, which was introduced in 2012 to efficiently execute machine learning and data mining algorithms in parallel across distributed systems. GraphLab allows for asynchronous, dynamic, graph-parallel computation while maintaining data consistency. It addresses key properties of machine learning algorithms like graph-structured computation and asynchronous iterative computation. The framework represents program state as a data graph and uses update functions to modify data within vertex scopes. Consistency models are introduced to ensure serializability while optimizing parallel execution. Performance tests showed GraphLab could achieve 1-2 orders of magnitude speedup over Hadoop implementations.
Detailed Information
| Author: | ['Kashif Rabbani'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 4 |
| Language: | English |
| Format: | |
| Price: | FREE |
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