Parallel and high-performance computing
Summary
This course provides insight into a broad variety of High Performance Computing (HPC) concepts and the majority of modern HPC architectures. Moreover, the student will learn to have a feeling about what architectures are suited for several types of algorithms and learn to program for them.
Content
HPC overview:
- Today's HPC: Beowulf-style clusters, massively parallel architectures, hybrid computing, accelerators
- HPC history and background
- HPC benchmarks explained
- Multicore systems
- Scaling
Writing HPC code:Shared memory parallelism with OpenMP
- Distributed memory parallelism with MPI
- Hybrid programming with OpenMP and MPI
- GPGPU primer
- Profiling
Keywords
HPC, Parallelization, MPI, GPU
Learning Prerequisites
Required courses
- Analysis, bachelor level
- Numerical analysis for engineers
- Matrix algebra
Recommended courses
- Programming concepts in scientific computing
Learning Outcomes
By the end of the course, the student must be able to:
- Classify the types of HPC architecture
- Identify codes suited for parallelization
- Apply the most common parallelization techniques
- Implement algorithms in parallel
- Investigate the performance of parallel code
- Argue about the differences in performance between theory and practice
- Optimize the usage of hardware and software resources depending on the type of algorithm to parallelize
Transversal skills
- Set objectives and design an action plan to reach those objectives.
- Communicate effectively with professionals from other disciplines.
- Access and evaluate appropriate sources of information.
- Write a scientific or technical report.
Teaching methods
Lectures, exercises, project work
Expected student activities
Attendance to lectures, completing exercises, writing a project
Assessment methods
Graded exercises, final project, and oral defense of project
Supervision
| Office hours | Yes |
| Assistants | Yes |
| Forum | Yes |
In the programs
- Semester: Spring
- Exam form: Oral (summer session)
- Subject examined: Parallel and high-performance computing
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- TP: 1 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: Oral (summer session)
- Subject examined: Parallel and high-performance computing
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- TP: 1 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: Oral (summer session)
- Subject examined: Parallel and high-performance computing
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- TP: 1 Hour(s) per week x 14 weeks
- Type: optional