Teaching Assistants

  • Chenyu Yang

Class times & rooms

  • Lecture: Monday, 14:15-16:00 (Zoom link), Classroom: ML H44
  • Exercise: Will begin on the second week of classes. Monday, 10:15-12:00 (Zoom link), Classroom: ML H44
  • Lecture recordings can be found below.
  • Office Hours:
    • Chenyu: Fridays 4-5 pm (on demand – send email: chenyang [at] student.ethz.ch).
    • Athena: on demand – send email: eceva [at] ethz.ch 
    • Sergio: on demand – send email: martiser [at] ethz.ch

Resources

Moodle Course: https://moodle-app2.let.ethz.ch/course/view.php?id=19891
Gitlab Repository: https://gitlab.ethz.ch/hpcse-public-repos/hpcse-ii-spring-2023-lecture 

 


Lectures

Date  Lecture
20.02.2023 Introduction & Intra-node data motion (slides) (recording)
27.02.2023 Communication-Tolerant Programming (slides) (recording)
06.03.2023 UQ and Optimisation (slides) (recording)
13.03.2023 Bayesian UQ and Sampling methods (slides updated 14/3/2021) (recording) (Notes on UQ)
20.03.2023 Sampling Strategies and The Korali Framework (slides) (recording)
27.03.2023 Data-Level Parallelism / Vectorization (slides) (recording)
03.04.2023  
24.04.2023  
08.05.2023  
15.05.2023  
22.05.2023  
 

Exercise & Tutorial Sessions

Date Session
27.02.2023 Tutorial 1: Hybrid MPI+OpenMP (material) (recording)
06.03.2023 Tutorial 2: Communication Cost (material) (recording)
13.03.2023 Tutorial 3: Probabilities and Bayesian inference (material) (recording)
20.03.2023 Tutorial 4: Introduction to the Korali framework (material) (recording)
27.03.2023 Tutorial 5: Optimization and Sampling with the Korali framework (material) (recording)
03.04.2023  
17.04.2023  
24.04.2023  
08.05.2023  
15.05.2023  
22.05.2023  
 

Homework

# Issue Date Due Date Homework
1 27.02.2023 13.03.2023 HW 1
2 13.03.2023 27.03.2023 HW 2 (updated point distribution 14/3/2023)
3 27.03.2023 17.04.2023 HW 3
4 17.04.2023    
5 08.05.2023    

Textbooks & Recommended Reads

  • Kowarschik, M. and Weiß, C., An overview of cache optimization techniques and cache-aware numerical algorithms.
  • Parallel Programming in MPI and OpenMP, V. Eijkhout.

  • CUDA C Programming Guide, NVIDIA
  • CUDA by example, J. Sanders and E. Kandrot

  • Data Analysis: A Bayesian Tutorial, Devinderjit Sivia

  • Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein, CRC Press, 2011

  • Introduction to Parallel Programming

  • Computer Organization and Design, D.H. Patterson and J.L. Hennessy