Recommended for
Graduate students interested in parallel and distributed computing
on large data sets represented as data streams or as graphs.
Special emphasis will be on programming abstractions and frameworks,
and on efficient implementations for parallel, distributed and heterogeneous systems.
This is an Advanced Course. It requires a previous master or PhD
level course in foundations, algorithms and programming for parallel systems,
such as TDDC78/TDDE65 Programming of Parallel Computers,
TDDD56 Multicore and GPU Computing, or
TDDE31 Big Data Analytics.
Organization
Introductory lecture about foundations (ca. 4-8h).
Student presentation, opposition and summary of a recent research paper.
Miniproject with demo and written summary.
The course was last given
This is a new course.
Goals
TBD, for now see the IDA graduate portal page for the course description.
Prerequisites
A previous master or PhD level course in foundations, algorithms and
programming of parallel and distributed systems, such as
TDDC78 Programming of Parallel Computers,
TDDD56 Multicore and GPU Programming or
TDDE31 Big-Data Analytics.
Good programming skills for the miniproject.
Contents/Schedule
TBD
Literature
Will be announced later.
Teachers
Examination
Credit
5hp if all examination moments are passed.
3hp if only TEN1 and UPG1 are passed (3hp).
UPG2 (miniproject) is optional (2hp, only in combination with passed TEN1 and UPG1).