Course syllabus adopted 2023-02-08 by Head of Programme (or corresponding).
Overview
- Swedish nameHög-prestanda parallell programmering
- CodeDAT400
- Credits7.5 Credits
- OwnerMPHPC
- Education cycleSecond-cycle
- Main field of studyComputer Science and Engineering, Software Engineering
- DepartmentCOMPUTER SCIENCE AND ENGINEERING
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 86111
- Maximum participants80 (at least 10% of the seats are reserved for exchange students)
- Block schedule
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0119 Laboratory 3 c Grading: UG | 3 c | 0 c | 0 c | 0 c | 0 c | 0 c | |
| 0219 Examination 4.5 c Grading: TH | 4.5 c | 0 c | 0 c | 0 c | 0 c | 0 c |
|
In programmes
Examiner
- Miquel Pericas
- Associate Professor, Computer and Network Systems, Computer Science and Engineering
Eligibility
General entry requirements for Master's level (second cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
The course DAT017 - Machine oriented programming or similar course is required. The course TDA384 - Principles for Concurrent programming is recommended.Aim
This course looks at parallel programming models, efficient programming methodologies and performance tools with the objective of developing highly efficient parallel programs.Learning outcomes (after completion of the course the student should be able to)
Knowledge and Understanding - List the different types of parallel computer architectures, programming models and paradigms, as well as different schemes for synchronization and communication. - List the typical steps to parallelize a sequential algorithm - List different methods for analysis methodologies of parallel program systems Competence and skills - Apply performance analysis methodologies to determine the bottlenecks in the execution of a parallel program - Predict the upper limit to the performance of a parallel program Judgment and approach - Given a particular software, specify what performance bottlenecks are limiting the efficiency of parallel code and select appropriate strategies to overcome these bottlenecks - Design energy-aware parallelization strategies based on a specific algorithms structure and computing system organization - Argue which performance analysis methods are important given a specific contextContent
The course consists of a set of lectures and laboratory sessions. The lectures start with an overview of parallel computer architectures and parallel programming models and paradigms. An important part of the discussion are mechanisms for synchronization and data exchange. Next, performance analysis of parallel programs is covered. The course proceeds with a discussion of tools and techniques for developing parallel programs in shared address spaces. This section covers popular programming environments such as pthreads and OpenMP. Next the course discusses the development of parallel programs for distributed address space. The focus in this part is on the Message Passing Interface (MPI). Finally, we discuss programming approaches for executing applications on accelerators such as GPUs. This part introduces the CUDA (Compute Unified Device Architecture) programming environment.The lectures are complemented with a set of laboratory sessions in which participants explore the topics introduced in the lectures. During the lab sessions, participants parallelize sample programs over a variety of parallel architectures, and use performance analysis tools to detect and remove bottlenecks in the parallel implementations of the programs.
Organisation
The teaching consists of theory-oriented lectures and lab sessions in which the participants develop code for different types of parallel computer systems
Literature
Parallel Programming for Multicore and Cluster Systems, Thomas Rauber, Gudula Rünger (2nd edition, 2013) https://www.springer.com/gp/book/9783642378003
Examination including compulsory elements
The course is examined by an individual written exam and a laboratory report written in groups.The final grade of the course is the same as the written exam. The laboratory report must have been approved in order to receive a final grade.
The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.