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Multicore and GPU Programming, 6 credits
Multicore- och GPU-Programmering, 6 hp
TDDD56
Main field of study
Information Technology Computer Science and Engineering Computer Science Media Technology and EngineeringCourse level
Second cycleCourse type
Programme courseExaminer
Christoph W. KesslerDirector of studies or equivalent
Ahmed RezineEducation components
Preliminary scheduled hours: 60 hRecommended self-study hours: 100 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
Main field of study
Information Technology, Computer Science and Engineering, Computer Science, Media Technology and EngineeringCourse 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
Computer engineering. Data structures and algorithms. Concurrent programming and operating systems. Programming skills in C. Some basic familiarity with C++ is useful.
Intended learning outcomes
Modern computers feature processors with multiple cores and powerful many-core based hardware accelerators such as graphics processing units (GPUs) that can be used for general-purpose computations (GPGPU or GPU Computing). The performance potential of such architectures can only be leveraged for speeding up applications if the code is properly parallelized and (re)written to exploit the specific architectural features.
After this course, the student will:
- be able to write code and re-write code for multicore and GPGPU architectures
- understand parallel algorithms and data structures, and be able to analyze them
- know general principles for parallel computing and techniques for parallelization.
Course content
Introduction to multi-core, many-core and GPU architecture concepts. Theory of parallel computing. Theory of parallelization. Design and analysis of parallel algorithms. Survey of parallel programming language concepts. Thread programming for multicore computing. SIMD-programming and data-parallel programming. GPU-programming with OpenCL and/or CUDA. Non-blocking synchronization and transactional memory. Scheduling for multicore and operating system issues. Introduction to heterogeneous multicore and
parallel DSP architecture concepts and programming.
Teaching and working methods
A lecture series introduces the theory and gives an overview of architectural concepts and programming techniques. A lab series contains programming assignments in multi-core thread programming and GPU programming. Lessons introduce the technical programming platforms used for the labs.
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
Four-grade scale, LiU, U, 3, 4, 5Other information
Supplementary courses: Programming of parallel computers - methods and tools is complementary to this course. Both address parallel computing, where this course emphasizes thread programming and GPU programming while TDDC78 focuses on OpenMP and message passing as required for programming larger clusters.
Department
Institutionen för datavetenskapDirector of Studies or equivalent
Ahmed RezineExaminer
Christoph W. KesslerCourse website and other links
http://www.ida.liu.se/~TDDD56Education components
Preliminary scheduled hours: 60 hRecommended self-study hours: 100 h
Course literature
Additional literature
Other
| 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.
Additional literature
Other
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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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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