Provided by: Association for Computing Machinery
Date Added: Aug 2014
Histograms are used in various fields to quickly pro le the distribution of a large amount of data. However, it is challenging to efficiently utilize abundant parallel resources in modern processors for histogram construction. To make matters worse, the most efficient implementation varies de-pending on input parameters (e.g., input distribution, number of bins and data type) or architecture parameters (e.g., cache capacity and SIMD width).This paper presents versatile histogram methods that achieve competitive performance across a wide range of input types and target architectures.