MA32070: Scientific computing
[Page last updated: 22 May 2025]
| Academic Year: | 2025/26 |
| Owning Department/School: | Department of Mathematical Sciences |
| Credits: | 5 [equivalent to 10 CATS credits] |
| Notional Study Hours: | 100 |
| Level: | Honours (FHEQ level 6) |
| Period: |
- Semester 2
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| Assessment Summary: | CWSI 100% |
| Assessment Detail: |
- Coursework 1 (CWSI 40%)
- Coursework 2 (CWSI 60%)
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| Supplementary Assessment: |
- Like-for-like reassessment (where allowed by programme regulations)
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| Requisites: |
While taking this module you must take MA32066
In taking this module you cannot take MA40177
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| Learning Outcomes: |
By the end of the course, you will be able to:
- Implement efficient numerical algorithms for large-scale problems in a modern programming language.
- Identify suitable abstractions and employ state-of-the-art software engineering techniques to write sustainable code.
- Analyse the complexity and efficiency of code by using suitable performance models.
- Understand and use parallel computer architectures and programming paradigms.
- Use optimised scientific libraries.
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| Synopsis: | You will develop the coding skills necessary for the computational solution of challenging scientific and engineering problems. You will learn how to use suitable software abstractions and libraries to implement code in an efficient and sustainable way. You will gain an understanding of parallel computing architectures and modern programming techniques.
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| Content: | Programming in Python and NumPy.
Algorithms for solving problems in numerical linear algebra and partial differential equations (PDEs), including iterative methods and storage formats for sparse matrices.
Analysis of algorithms: complexity, performance analysis (roofline model), floating point arithmetic and rounding errors.
Parallel computation: principles, message-passing model, parallel data structures, scheduling on clusters, parallel performance indicators.
Parallel data structures (vectors and matrices) and algorithms.
Abstractions and modern software engineering techniques.
Use of bespoke high-performance libraries such as PETSc and hypre.
Case studies drawn from real-life problems in science and engineering will illustrate how the techniques taught in this course can be used to solve challenging problems.
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| Course availability: |
MA32070 is Optional on the following courses:
Department of Mathematical Sciences
- USMA-AAB16 : BSc(Hons) Mathematical Sciences with Study year abroad (Year 4)
- USMA-AKB16 : BSc(Hons) Mathematical Sciences with Year long work placement (Year 4)
- USMA-AFB30 : BSc(Hons) Mathematics (Year 3)
- USMA-AAB14 : BSc(Hons) Mathematics with Study year abroad (Year 4)
- USMA-AKB14 : BSc(Hons) Mathematics with Year long work placement (Year 4)
- USMA-AFB32 : BSc(Hons) Mathematics and Statistics (Year 3)
- USMA-AAB02 : BSc(Hons) Mathematics and Statistics with Study year abroad (Year 4)
- USMA-AKB02 : BSc(Hons) Mathematics and Statistics with Year long work placement (Year 4)
- USMA-AFB33 : BSc(Hons) Mathematics, Statistics and Data Science (Year 3)
- USMA-AAB20 : BSc(Hons) Mathematics, Statistics, and Data Science with Study year abroad (Year 4)
- USMA-AKB20 : BSc(Hons) Mathematics, Statistics, and Data Science with Industrial Placement (Year 4)
- USMA-AFM30 : MMath(Hons) Mathematics (Year 3)
- USMA-AKM15 : MMath(Hons) Mathematics with Year long work placement (Year 4)
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Notes: - This unit catalogue is applicable for the 2025/26 academic year only. Students continuing their studies into 2026/27 and beyond should not assume that this unit will be available in future years in the format displayed here for 2025/26.
- 好色tv and units are subject to change in accordance with normal University procedures.
- Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
- Find out more about these and other important University terms and conditions here.
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