Main field(s) of study and in-depth level:
Computer Science A1F,
Computational Science A1F
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
G1N: has only upper-secondary level entry requirements
G1F: has less than 60 credits in first-cycle course/s as entry requirements
G1E: contains specially designed degree project for Higher Education Diploma
G2F: has at least 60 credits in first-cycle course/s as entry requirements
G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
GXX: in-depth level of the course cannot be classified
A1N: has only first-cycle course/s as entry requirements
A1F: has second-cycle course/s as entry requirements
A1E: contains degree project for Master of Arts/Master of Science (60 credits)
A2E: contains degree project for Master of Arts/Master of Science (120 credits)
AXX: in-depth level of the course cannot be classified
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
The Faculty Board of Science and Technology
120 credits. High Performance and Parallel Computing 7.5 credits or High Performance Programming 10 credits. Proficiency in English equivalent to the Swedish upper secondary course English 6.
On completion of the course the student shall be able to:
write efficient code directly adapted to a specific accelerator type to solve specific problems;
use frameworks that offer abstractions of accelerator technology;
identify and motivate strengths and weaknesses of different accelerator architectures for given problems;
reason about data locality in heterogeneous systems;
measure and improve the performance of self-written and framework-based accelerator code;
Orientation about characteristics for, among other things, the architecture types CPU, GPU, TPU, wide vector instructions. Memory architectures and the relationship between the host CPU and other devices. CUDA programming and explicit vector operations. Use of TensorFlow to solve optimization problems and calculation of major mathematical expressions. Solutions in traditionally compiled and interpreted languages to use accelerators. Profiling of the relevant solutions.
Lectures, computer labs, assignments and projects.
Oral presentation of laboratory work and projects. Written assignments and project report. Laboratory work and assignments (5 credits) and projects (2.5 credits).
If there are special reasons, the examiner may make an exception from the specified examination method and allow an individual student to be examined in another way. Special reasons may, for example, be information about special pedagogical support from the university's coordinator for students with disabilities.
The reading list is missing. For further information, please contact the responsible department.