General Information
| Course Code | XM_40017 |
|---|---|
| Credits | 6 EC |
| Period | P1 |
| Course Level | 400 |
| Language of Tuition | English |
| Faculty | Faculty of Science |
| Course Coordinator | dr. F. Verdugo Rojano |
| Examiner | dr. F. Verdugo Rojano |
| Teaching Staff |
dr. F. Verdugo Rojano prof. dr. ir. H.E. Bal |
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Part of programme(s)
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Practical Information
You need to register for this course yourself. The (de)registration deadlines can be found on VU.nl
| Teaching Methods | Lecture |
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Target audiences
This course is also available as:
Course Objective
At the end of the course, students will be able to
- Understand the main principles of High Performance Computing (Knowledge and understanding)
- Design and implement efficient parallel algorithms on distributed-memory parallel computers (Applying knowledge and understanding)
- Recognize and compare different parallel computer architectures (Knowledge and understanding, Making judgements)
- Make use of different parallel programming models and environments such as MPI and Julia’s task-based distributed-computing model (Applying knowledge and understanding).
- Analyze the performance of parallel applications and network topologies (Making judgements)
- Identify the main computational challenges in selected parallel computing applications (Knowledge and understanding)
- Design parallel algorithms using the Julia Programming Language (Applying knowledge and understanding)
Course Content
This course discusses how to write computer programs that can run in parallel on a large number of processors with the main goal of reducing execution time and solving large problems that cannot fit in a single compute node. You will learn how to design such parallel algorithms in order to minimize communication overhead and load imbalance, how to implement them using different parallel computing models, and how to analyze the performance of the resulting codes and the underlying hardware. Among all parallel programming models and environments available for High Performance Computing, this course focuses on distributed-memory parallelization. You will learn established techniques such as message-passing with MPI, but also more modern approaches like the task-based distributed-computing model provided by the Julia programming language. Several parallel applications are discussed in the course, including partial differential equations, deep learning, N-body simulation, and search algorithms.
Additional Information Teaching Methods
- There are 2 class sessions per week. Part of the course follows the flipped classroom method. Pre-recordings, lecture notes, and exercises are made available in advance for self study, and follow up discussions take place in class meetings to deepen understanding.
- The course also includes the opportunity to run experiments on the DAS computer cluster.
- Computer codes used in the course are implemented in the Julia programming language. Previous knowledge of Julia is not required as it will be learned as part of the course.
Method of Assessment
Written exam.Literature
Course material will be made available on Canvas.
Additional Information Target Audience
Master Business Analytics
Master Computational Science
Master Computer Science
Master Computer Security
Additional Information
There is also a separate Parallel Programming Practical (6 EC) in Period
3 whose aim is to complement the contents of this course with further practical
skills and experience.