An asymptotically optimal parallel bin-packing algorithm

Abstract

The authors introduce a bin-packing heuristic that is well-suited for implementation on massively parallel SIMD (single-instruction multiple-data) or MIMD (multiple-instruction multiple-data) computing systems. The average-case behavior (and the variance) of the packing technique can be predicted when the input data have a symmetric distribution. The method is asymptotically optimal, yields perfect packings, and achieves the best possible average case behavior with high probability. The analytical result improves upon any online algorithms previously reported in the literature and is identical to the best results reported so far for offline algorithms.<>