CME 323: Distributed Algorithms and Optimization
Spring 2018, Stanford University
Tue, Thu 12:00 PM - 1:20 PM at 260-113 (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.
We will focus on the analysis of parallelism and distribution costs of algorithms.
Sometimes, topics will be illustrated with exercises
using Apache Spark and TensorFlow.
Pre-requisites: Targeting graduate students having
taken Algorithms at the level of CME 305 or CS 161.
Being able to competently program in any main-stream high level language.
There will be homeworks, a midterm, and a final exam.
The midterm will be in class on Tuesday May 8th.
by Guy E. Blelloch and Bruce M. Maggs [BB]
Introduction to Algorithms by Cormen, Leiserson, Rivest, Stein [CLRS]
by Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia [KKWZ]
TensorFlow for Deep Learning
by Bharath Ramsundar and Reza Zadeh [RZ]
Homework 1 [pdf] [tex] Due April 19th. [soln]
Homework 2 [pdf] [tex] Due May 3rd. [soln]
Homework 3 [pdf] [tex] Due May 22nd.
Lectures and References
- Lecture 1: Fundamentals of Distributed and Parallel algorithm analysis. Reading: BB Chapter 1.
- Lecture 2: Scalable algorithms, Scheduling. Reading: BB 5.
Handbook of Scheduling,
Better Bounds for Online Scheduling
- Lecture 3: Prefix Sum, Mergesort. Reading: KT 5, BB 8.
Cole's parallel merge sort (1988)
- Lecture 4: Introduction to TensorFlow.
Guest lecture by Bharath Ramsundar. Lecture Slides
- Lecture 5: Parallel quick-select, Quicksort, Strassen's Algorithm, Minimum Spanning Trees. Reading: KT 3, 4.5, 4.6.
Lecture Notes (a), Lecture Notes (b),
Linear time bounds for median select,
Prefix scan qsort,
- Lecture 6: Graph contraction, star contraction, MST algorithms. Reading: CLRS 12, 13.
- Lecture 7: (Stochastic) Gradient Descent, Parallel SGD (HOGWILD!). HOGWILD!, Omnivore.
- Lecture 8: Intro to distributed computing, sampling, communication patterns.
- Lecture 9: Distributed sort, intro to map reduce, applications to map reduce.
- Lecture 10: Map Reduce (indexing), Sparse Matrix Multiplies using SQL, Joins using Map Reduce.
- Lecture 11: Midterm
- Lecture 12: Joins using map reduce, measures of complexity, triangle counting.
Curse of the Last Reducer.
- Lecture 13: Sparse matrix multiplication, triangle counting, measures of complexity.
Sparse matrix multiplication using SQL, Sparse matrix multiplication in MapReduce.
- Lecture 14: Intro to Spark and other Distributed Computing Tools, Gradient Descent in Spark, Communication Patterns
Intro to Spark, Intro to Distributed Optimization, Communication Patterns.
Spring 2015: [class webpage]
Spring 2016: [class webpage]
Spring 2017: [class webpage]
Reza: rezab at stanford
Office hours: by appointment
Yokila Arora: yarora at stanford
TA office hours will be held in the Huang Engineering Center basement
(in front of the ICME office)
Office hours: Monday 12-2, Wednesday 2-4.