Adaptive Data Partitioning Using Probability Distribution

Many computing problems benefit from dynamic data partitioning—dividing a large amount of data into smaller chunks with better locality. When data can be sorted, two methods are commonly used in partitioning. The first selects pivots, which enable balanced partitioning but cause a large overhead of up to half of the sorting time. The second method uses simple functions, which is fast but requires that the input data confirm to a uniform distribution. In this paper, we propose a new method, which partitions data using the cumulative distribution function. It partitions data of any distribution in linear time, independent to the number of sublists to be partitioned into. Experiments show 10-30% improvement in partitioning balance and 20-70% reduction in partitioning overhead. The new method is more scalable than existing methods. It yields greater benefit when the data set and the number of sub-lists grow larger. By applying this method, our sequential sorting beats Quick-sorting by 20% and parallel sorting exceeds the previous sorting algorithm by 33-50%.