Systems and Methods for Efficient Data Preprocessing of Machine Learning Workloads
US-2024403138-A1 · Dec 5, 2024 · US
US8938739B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-8938739-B2 |
| Application number | US-201213487185-A |
| Country | US |
| Kind code | B2 |
| Filing date | Jun 2, 2012 |
| Priority date | Jun 2, 2012 |
| Publication date | Jan 20, 2015 |
| Grant date | Jan 20, 2015 |
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A multi processor task allocation method is described that considers task dependencies while performing task allocation in order to avoid blocking of a task's execution while waiting for the resolution of the dependency. While allocating the tasks to the processors the potential blocking time is considered, and the best allocation that will have the least amount of blocking time is found.
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What is claimed is: 1. A method of multiprocessor task partitioning comprising the steps of: calculating processor utilization for each task; combining tasks sharing a common resource to form composite tasks; calculating processor utilization for each composite task; arranging not combined tasks and the composite tasks in a non increasing order of their processor utilization in an array; applying a worst fit decreasing heuristic on the tasks in said array; using a respon…
Physics · mapped topic
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Physics · mapped topic
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