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Programming of Parallel Computers - Methods and Tools, 6 credits
Programmering av parallelldatorer - metoder och verktyg, 6 hp
TDDC78
Main field of study
Computer Science and Engineering Computer ScienceCourse level
Second cycleCourse type
Programme courseExaminer
Christoph W. KesslerDirector of studies or equivalent
Ahmed RezineEducation components
Preliminary scheduled hours: 52 hRecommended self-study hours: 108 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
Main field of study
Computer Science and Engineering, Computer ScienceCourse level
Second cycleAdvancement level
A1XCourse offered for
- Computer Science and Engineering, M Sc in Engineering
- Industrial Engineering and Management - International, M Sc in Engineering
- Industrial Engineering and Management, M Sc in Engineering
- Media Technology and Engineering, M Sc in Engineering
- Applied Physics and Electrical Engineering, M Sc in Engineering
- Computer Science, Master's programme
- Electronics Engineering, Master's programme
- Mathematics, Master's programme
- Information Technology, M Sc in Engineering
- Applied Physics and Electrical Engineering - International, M Sc in Engineering
- Computer Science and Software Engineering, M Sc in Engineering
Entry requirements
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.
Prerequisites
Basic course in programming. A course in process programming is useful but not required, since understanding the process concept is assumed. Programming skills in C/C++ are required.Intended learning outcomes
Parallel computers are used for heavy computations.
The student should acquire knowledge about the programming of parallel computers and master selected methods and tools.
The course shall also give an overview of how parallel computers can be used in some application areas, such as image analysis and scientific computations.
After the course the student is expected to be able to 1) use efficient methods and languages for the programming of parallel computers, 2) program parallel computers with distributed memory (MPI) and shared memory (OpenMP).
Course content
Parallel computer architecture: memory hierarchies, shared memory and distributed memory architectures. Vector instructions. Parallel execution models and programming languages. Performance measurements and enhancement. Message passing, multithreaded and dataparallel programming. Principles of dataparallel languages. Time complexity. Scalability. Scheduling of parallel programs. Grid computing. Tools for parallel programming. MPI (Message passing interface), HPF (High Performance Fortran) and OpenMP. Basic parallel algorithms and BLAS (Basic Linear Algebra Subprograms). Application areas. Parallel solving of equation systems. The laboratory course gives practical experience in programming parallel systems (different programming paradigms are used).
Teaching and working methods
The lectures deal with theory and principles, and the laboratory course gives practical exercise in parallel programming and the use of support systems.
The lab course uses parallel supercomputer resources located at the National Supercomputer Center.
Examination
| LAB1 | Laboratory work | 3 credits | U, G |
| TEN1 | Written examination | 3 credits | U, 3, 4, 5 |
The questions in the written exam check how well the student has fulfilled the learning goals of the course. For passing the exam, deficits in fulfilling certain partial goals can be balanced by a better fulfilling of other partial goals.
Grades
Alternative-grade scale, LiU, U, 3, 4, 5Other information
| Supplementary courses: Multicore and GPU Programming. |
Department
Institutionen för datavetenskapDirector of Studies or equivalent
Ahmed RezineExaminer
Christoph W. KesslerCourse website and other links
http://www.ida.liu.se/~TDDC78/Education components
Preliminary scheduled hours: 52 hRecommended self-study hours: 108 h
Course literature
C. Kessler: Programming of parallel computers - Compendium OHs, finns tillgänglig för registrerade kursdeltagare på kurshemsidan. L. Elden, H. Park and Y. Saad. Scientific Computing on High Performance Computers (compendium), finns tillgänglig för registrerade kursdeltagare på kurshemsidan. Labb-kompendium, finns på kurshemsidan. För ytterligare kurslitteratur se kursens hemsida.| Code | Name | Scope | Grading scale |
|---|---|---|---|
| LAB1 | Laboratory work | 3 credits | U, G |
| TEN1 | Written examination | 3 credits | U, 3, 4, 5 |
The questions in the written exam check how well the student has fulfilled the learning goals of the course. For passing the exam, deficits in fulfilling certain partial goals can be balanced by a better fulfilling of other partial goals.
Regulations (apply to LiU in its entirety)
The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.
LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva.
Course literature is preliminary.
C. Kessler: Programming of parallel computers - Compendium OHs,
finns tillgänglig för registrerade kursdeltagare på kurshemsidan.
L. Elden, H. Park and Y. Saad. Scientific Computing on High Performance Computers (compendium), finns tillgänglig för registrerade kursdeltagare på kurshemsidan.
Labb-kompendium, finns på kurshemsidan.
För ytterligare kurslitteratur se kursens hemsida.
Note: The course matrix might contain more information in Swedish.
I = Introduce, U = Teach, A = Utilize
| I | U | A | Modules | Comment | ||
|---|---|---|---|---|---|---|
| 1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
| 1.1 Knowledge of underlying mathematics and science (G1X level) |
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| 1.2 Fundamental engineering knowledge (G1X level) |
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X
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| 1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
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X
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X
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| 1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
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| 1.5 Insight into current research and development work |
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| 2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
| 2.1 Analytical reasoning and problem solving |
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X
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X
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| 2.2 Experimentation, investigation, and knowledge discovery |
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X
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| 2.3 System thinking |
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X
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X
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| 2.4 Attitudes, thought, and learning |
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| 2.5 Ethics, equity, and other responsibilities |
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| 3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
| 3.1 Teamwork |
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X
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| 3.2 Communications |
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| 3.3 Communication in foreign languages |
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| 4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
| 4.1 External, societal, and environmental context |
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| 4.2 Enterprise and business context |
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| 4.3 Conceiving, system engineering and management |
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X
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X
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| 4.4 Designing |
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X
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X
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| 4.5 Implementing |
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X
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X
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| 4.6 Operating |
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| 5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
| 5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
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| 5.2 Economic conditions for knowledge development |
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| 5.3 Identification of needs, structuring and planning of research or development projects |
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| 5.4 Execution of research or development projects |
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| 5.5 Presentation and evaluation of research or development projects |
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