class CostBasedOptimizer extends Optimizer with Logging
Experimental cost-based optimizer that aims to avoid moving sections of the plan to the GPU when it would be better to keep that part of the plan on the CPU. For example, we don't want to move data to the GPU just for a trivial projection and then have to move data back to the CPU on the next step.
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- new CostBasedOptimizer()
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def
optimize(conf: RapidsConf, plan: SparkPlanMeta[SparkPlan]): Seq[Optimization]
Walk the plan and determine CPU and GPU costs for each operator and then make decisions about whether operators should run on CPU or GPU.
Walk the plan and determine CPU and GPU costs for each operator and then make decisions about whether operators should run on CPU or GPU.
- conf
Rapids configuration
- plan
The plan to optimize
- returns
A list of optimizations that were applied
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