Lower Bounds for Distributed Maximum-Finding Algorithms

Jan K. Pachl; E. Korach; D. Rotem

doi:10.1145/1634.1889

Journal of the ACM

Paper

20 Sep 1984

Lower Bounds for Distributed Maximum-Finding Algorithms

Download paper

Abstract

Tills paper establishes several lower bounds of the form Ω(nlogn) for the number of messages needed to find the maximum label in a circular configuration of n labeled processes with no central controller. © 1984, ACM. All rights reserved.

Conference paper

Program equivalence and context-free grammars

Barry K. Rosen

SWAT 1972

Conference paper

LLM-Pilot: Characterize and Optimize Performance of your LLM Inference Services

Gosia Lazuka, Andreea Simona Anghel, et al.

SC 2024

Paper

Basis scaling and double pruning for efficient inference in network-based transfer learning

Ken C.L. Wong, Satyananda Kashyap, et al.

Pattern Recognition Letters

Paper

Conditional generative models for learning stochastic processes

Salvatore Certo, Anh Pham, et al.

Quantum Machine Intelligence

View all publications

Abstract

Related

Program equivalence and context-free grammars

LLM-Pilot: Characterize and Optimize Performance of your LLM Inference Services

Basis scaling and double pruning for efficient inference in network-based transfer learning

Conditional generative models for learning stochastic processes