Accelerator-Based Programming
Course, Master's level, 1TD054
Expand the information below to show details on how to apply and entry requirements.
Autumn 2025 Autumn 2025, Uppsala, 33%, On-campus, English
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 1 September 2025–2 November 2025
- Language of instruction
- English
- Entry requirements
-
120 credits. High Performance and Parallel Computing or High Performance Programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Selection
-
Higher education credits in science and engineering (maximum 240 credits)
- Fees
- If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 12,083
- Total tuition fee: SEK 12,083
- Application deadline
- 15 April 2025
- Application code
- UU-12000
Admitted or on the waiting list?
- Registration period
- 25 July 2025–7 September 2025
- Information on registration from the department
Autumn 2025 Autumn 2025, Uppsala, 33%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 1 September 2025–2 November 2025
- Language of instruction
- English
- Entry requirements
-
120 credits. High Performance and Parallel Computing or High Performance Programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 25 July 2025–7 September 2025
- Information on registration from the department
About the course
Historically, data analysis and computing-related tasks have been executed on the CPU. With increasing data volumes, the interest in using various other computational platforms has increased. One important example of this is the use of GPUs, originally graphics processing units, for machine learning (GPU stands for Graphics Processing Unit).
Sometimes, one can get adequate or even great performance for a specific task by using an existing framework that supports an accelerator, such as a GPU. However, frequently it can be beneficial to write customised accelerator code. In this course, we review various accelerator types and compare them to traditional CPUs. We also explore the CPU/accelerator interface, and how we can program and profile performance on accelerators. Profiling is of uttermost importance in an accelerator context, since it is frequently a great challenge to actually unlock the theoretical gains in efficiency promised by the accelerators.
Reading list
No reading list found.