Optimizing Sorting with Genetic Algorithms

The growing complexity of modern processors has made the generation of highly efficient code increasingly difficult. Manual code generation is very time consuming, but it is often the only choice since the code generated by today’s compiler technology often has much lower performance than the best hand-tuned codes. A promising code generation strategy, implemented by systems like ATLAS, FFTW, and SPIRAL, uses empirical search to find the parameter values of the implementation, such as the tile size and instruction schedules, that deliver near-optimal performance for a particular machine. However, this approach has only proven successful on scientific codes whose performance does not depend on the input data. In this paper we study machine learning techniques to extend empirical search to the generation of sorting routines, whose performance depends on the input characteristics and the architecture of the target machine. We build on a previous study that selects a ”pure” sort...

• Type: Conference
• Year: 2005
• Where: CGO
• Authors: Xiaoming Li, María Jesús Garzarán, David A. Padua
• Keywords: CGO 2005, Code Generation, Input Data, Software Engineering, Sorting Algorithms