CME 323: Distributed Algorithms and OptimizationSpring 2018, Stanford UniversityTue, Thu 12:00 PM  1:20 PM at 260113 (04/02/2018  06/06/2018) Instructor: Reza Zadeh
Computer Science is evolving to utilize new hardware such as GPUs, TPUs, CPUs, and large commodity clusters thereof. Many subfields such as Machine Learning and Optimization have adapted their algorithms to handle such clusters. Topics include distributed and parallel algorithms for: Optimization, Numerical Linear Algebra, Machine Learning, Graph analysis, Streaming algorithms, and other problems that are challenging to scale on a commodity cluster. The class will focus on analyzing programs, with some implementation using Apache Spark and TensorFlow. The course will be split into two parts: first, an introduction to fundamentals of parallel algorithms and runtime analysis on a single multicore machine. Second, we will cover distributed algorithms running on a cluster of machines. Class FormatWe will focus on the analysis of parallelism and distribution costs of algorithms. Sometimes, topics will be illustrated with exercises using Apache Spark and TensorFlow. Prerequisites: Targeting graduate students having taken Algorithms at the level of CME 305 or CS 161. Being able to competently program in any mainstream high level language. There will be homeworks, a midterm, and a final exam.
Grade Breakdown: The midterm will be in class.
Textbooks: HomeworkHomework 1 Homework 2 Homework 3 Homework 4 Lectures and References
Previous YearsSpring 2015: [class webpage] Spring 2016: [class webpage] Spring 2017: [class webpage] 
