Optimizing Sorting with Machine Learning Algorithms

The growing complexity of modern processors has made the development of highly efficient code increasingly difficult. Manually developing highly efficient code is usually expensive but necessary due to the limitations of today’s compilers. A promising automatic code generation strategy, implemented by library generators such as ATLAS, FFTW, and SPIRAL, relies on empirical search to identify, for each target machine, the code characteristics, such as the tile size and instruction schedules, that deliver the best performance. This approach has mainly been applied to scientific codes which can be optimized by identifying code characteristics that depend only on the target machine. In this paper, we study the generation of sorting routines whose performance also depends on the characteristics of the input data. We present two approaches to generate efficient sorting routines. First, we consider the problem of selecting the best “pure” sorting algorithm as a function of the char...

• Type: Conference
• Year: 2007
• Where: IPPS
• Authors: Xiaoming Li, María Jesús Garzarán, David A. Padua
• Keywords: Distributed And Parallel Computing, Input Data, IPPS 2007, Sorting Algorithms, Target Machine