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 barrett and zorn


Predicting Lifetimes in Dynamically Allocated Memory

Neural Information Processing Systems

Predictions oflifetimes of dynamically allocated objects can be used to improve time and space efficiency of dynamic memory manage(cid:173) ment in computer programs. Barrett and Zorn [1993] used a simple lifetime predictor and demonstrated this improvement on a variety of computer programs. In this paper, we use decision trees to do lifetime prediction on the same programs and show significantly better prediction . Our method also has the advantage that during training we can use a large number of features and let the decision tree automatically choose the relevant subset. Dynamic memory allocation is used in many computer applications.


Predicting Lifetimes in Dynamically Allocated Memory

Neural Information Processing Systems

Predictions oflifetimes of dynamically allocated objects can be used to improve time and space efficiency of dynamic memory management in computer programs. Barrett and Zorn [1993] used a simple lifetime predictor and demonstrated this improvement on a variety of computer programs. In this paper, we use decision trees to do lifetime prediction on the same programs and show significantly better prediction. Our method also has the advantage that during training we can use a large number of features and let the decision tree automatically choose the relevant subset.


Predicting Lifetimes in Dynamically Allocated Memory

Neural Information Processing Systems

Predictions oflifetimes of dynamically allocated objects can be used to improve time and space efficiency of dynamic memory management in computer programs. Barrett and Zorn [1993] used a simple lifetime predictor and demonstrated this improvement on a variety of computer programs. In this paper, we use decision trees to do lifetime prediction on the same programs and show significantly better prediction. Our method also has the advantage that during training we can use a large number of features and let the decision tree automatically choose the relevant subset.


Predicting Lifetimes in Dynamically Allocated Memory

Neural Information Processing Systems

Predictions oflifetimes of dynamically allocated objects can be used to improve time and space efficiency of dynamic memory management incomputer programs. Barrett and Zorn [1993] used a simple lifetime predictor and demonstrated this improvement on a variety of computer programs. In this paper, we use decision trees to do lifetime prediction on the same programs and show significantly better prediction. Our method also has the advantage that during training we can use a large number of features and let the decision tree automatically choose the relevant subset.