parallel and distributed algorithms
Zero Lecture
The course of Parallel and Distributed Algorithms has prerequites: Data Structures, Algorithms, Discrete Mathematical structures, and Theory of Computation
Course Objectives
The course is aimed to impart the in depth advanced and design and Analysis knowledge about modern parallel and Distributed Algorithms running on multiprocessor architecture and in a distributed architecture.
Course Outcomes
The course will result in acquiring the advanced knowledge on the various topics in parallel and distributed algorithms.
Module 1: parallel algorithms
matching problemNetwork flows and maximum flow problems
assignment#1
geometrical algorithms: applications, divide and conquer, convexity
geometrical algorithsm: convex hull, proximity, voronoi diagrams
parallel algorithms (concepts, prams, interconnection networks)
parallel algorithms (work-depth models, design of parallel algorithms)
Randomized algorithms
random variable
home assignment#2
approximation algorithm
self adjusting, persistent and multidimensional data structres Introduction to Natural Language Processing-I