Accelerator-Based Programming
Course, Master's level, 1TD055
Autumn 2023 Autumn 2023, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 28 August 2023–30 October 2023
- Language of instruction
- English
- Entry requirements
-
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.
- 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.
- Application fee: SEK 900
- First tuition fee instalment: SEK 18,125
- Total tuition fee: SEK 18,125
- Application deadline
- 17 April 2023
- Application code
- UU-12020
Admitted or on the waiting list?
- Registration period
- 28 July 2023–4 September 2023
- Information on registration.
Autumn 2023 Autumn 2023, Uppsala, 50%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 28 August 2023–30 October 2023
- Language of instruction
- English
- Entry requirements
-
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.
Admitted or on the waiting list?
- Registration period
- 28 July 2023–4 September 2023
- Information on registration.
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.