Coursebooks

Parallelism and concurrency

CS-206

Lecturer(s) :

Kuncak Viktor
Odersky Martin

Language:

English

Summary

The course introduces parallel programming models, algorithms, and data structures, map-reduce frameworks and their use for data analysis, as well as shared-memory concurrency.

Content

See https://lara.epfl.ch/w/parcon17:top

Parallel programming & execution models

Functional parallelism

Data-level parallelism

Threads and fork/join parallelism

Synchronization

Threads and Shared Memory in Java

Futures

Large-Scale Parallel programming using Apache Spark

Keywords

Parallelism, threads, synchronization, locks, memory models.

Learning Prerequisites

Required courses

¿ Functional programming (CS-210)

¿ Algorithms (CS-250)

¿ Computer Architecture (CS-208)

Recommended courses

System oriented programming (CS-207)

Important concepts to start the course

Functional programming and functional data structures

Algorithms and data structures

Learning Outcomes

By the end of the course, the student must be able to:

• Construct parallel software.
• Perform tuning parallel software.

Teaching methods

Ex cathedra, labs, exercices

Assessment methods

Programming assignments (30%); final exam in August (70%)

Resources

Notes/Handbook

Lecture notes, copies of the slides