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An interpolation-based approach to multi-parameter performance modeling for heterogeneous systems

机译:基于插值的异构系统多参数性能建模方法

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To effectively optimize applications for emerging heterogeneous architectures, compilers and synthesis tools must perform the challenging task of estimating the performance of different implementations and optimizations for different numbers and types of computational resources. Many performance-prediction techniques exist, but those approaches are specific to particular resources or applications, and are often not capable of prediction for all combinations of inputs. In this paper, we introduce an approach to multi-parameter performance modeling based on sampling and interpolation. This approach can be used in conjunction with execution time data, simulated or observed, to quickly perform performance estimation for any function, on any resource, with any combination of inputs. By evaluating a Kriging-based interpolator on a variety of functions and computational resources, we determine bounds on the accuracy of this approach, and show that an interpolation-based approach utilizing Kriging can effectively model execution time for most applications. We also show that Kriging is a highly effective interpolation technique for execution time, and can be up to four orders of magnitude more accurate than nearest-neighbor interpolation or radial basis function interpolation.
机译:为了有效地为新兴的异构体系结构优化应用程序,编译器和综合工具必须执行具有挑战性的任务,即估算不同数量和类型的计算资源的不同实现和优化的性能。存在许多性能预测技术,但是这些方法是特定于特定资源或应用程序的,通常不能对输入的所有组合进行预测。在本文中,我们介绍了一种基于采样和插值的多参数性能建模方法。此方法可以与模拟或观察到的执行时间数据结合使用,以使用任何输入组合快速对任何资源上的任何功能执行性能评估。通过评估各种功能和计算资源上基于Kriging的插值器,我们确定了这种方法的精度范围,并表明利用Kriging的基于插值的方法可以有效地对大多数应用程序执行时间建模。我们还表明,克里格算法是一种高效的执行时间插值技术,比最近邻插值法或径向基函数插值法精度高四个数量级。

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