Randomized parallel selection [chapter]

Sanguthevar Rajasekaran
1990 Lecture Notes in Computer Science  
We show that selection on an input of size N can be performed on a P -node hypercube (P = N/(log N)) in time O(N/P ) with high probability, provided each node can process all the incident edges in one unit of time (this model is called the parallel model and has been assumed by previous researchers (e.g., [?])). This result is important in view of a lower bound of Plaxton that implies selection takes Ω((N/P ) log log P + log P ) time on a P -node hypercube if each node can process only one edge
more » ... at a time (this model is referred to as the sequential model).
doi:10.1007/3-540-53487-3_46 fatcat:ly44tx2lgfdahpzx7fvs4niuuy