Learning to Predict Performance from Formula Modeling and Training Data
Learning to Predict Performance from Formula Modeling and Training Data
复制标题
学习根据公式建模和训练数据预测性能
DOI：
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
M. Veloso
中科院分区：
文献类型：
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作者：
Bryan Singer;M. Veloso
本文报告了我们的工作，并恢复框架信号处理算法op timization作为机器学习任务作为机器学习任务，sin gle信号处理算法可以由许多不同的不同，但在数学上是等效的公式时，当这些公式在实际代码中实现时，它们具有非常di的实现。 ERENT运行时间信号过程Ing优化涉及一个公式，该公式不幸地将算法实现了数学公式及其运行时间之间的正确映射尚不清楚，但是可以收集各种公式的经验绩效数据，这是一个有趣的机会，可以在本文中学习预测运行时间绩效，我们在本文中提出了两个主要结果数学公式具有不同的特征集，这些公式将它们与划分的分区分配给了显着不同的运行时间，并且功能近似值可以学会准确地预测公式的运行时间给出了一组有限的培训数据，显示了选择不同功能以描述这项工作的影响
This paper reports on our work and re sults framing signal processing algorithm op timization as a machine learning task A sin gle signal processing algorithm can be repre sented by many di erent but mathematically equivalent formulas When these formulas are implemented in actual code they have very di erent running times Signal process ing optimization is concerned with nding a formula that implements the algorithm as ef ciently as possible Unfortunately a correct mapping between a mathematical formula and its running time is unknown However empirical performance data can be gathered for a variety of formulas This data o ers an interesting opportunity to learn to predict running time performance In this paper we present two major results along this direc tion Di erent sets of features are iden ti ed for mathematical formulas that distin guish them into partitions with signi cantly di erent running times and A function approximator can learn to accurately predict the running time of a formula given a limited set of training data Showing the impact of selecting di erent features to describe the in put this work contributes an extensive study on the role of learning for this novel task