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MA40177: Scientific computing

Follow this link for further information on academic years Academic Year: 2016/7
Further information on owning departmentsOwning Department/School: Department of Mathematical Sciences
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 2
Further information on unit assessment Assessment Summary: CW 100%
Further information on unit assessment Assessment Detail:
  • Class test (CW 30%)
  • Coursework 1 (CW 35%)
  • Coursework 2 (CW 35%)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites: Before taking this module you must take MA30051 (or take MA20222 and have equivalent experience subject to the approval of the unit convenor) or you must take MA50174 (or an equivalent unit from another institution).
Further information on descriptions Description: Aims:
To teach an understanding and appreciation of issues arising in the computational solution of challenging scientific and engineering problems.

Learning Outcomes:
Students should be able to write code to solve efficiently a range of scientific problems. They should be able to analyse algorithm complexity and efficiency. They should be familiar with scientific libraries and parallel programming. They will be expected to have deep knowledge of at least one challenging application.

Efficient solution of real-world problems using scientific computing (T, A); parallel programming (T, A).

Units, complexity, analysis of algorithms, benchmarks. Floating point arithmetic. Programming in Fortran90. Makefiles. Data structures, full and sparse matrices. BLAS and LAPACK libraries. Visualisation. Parallel Computation: principles, message passing model, MPI, parallel data structures, scheduling on clusters, performance indicators.
Other libraries/software on the web: NAG Library, Netlib, GAMS, ScaLAPACK, ARPACK, PETSc, hypre. Linking with other languages such as C, C++. Case studies illustrating the lectures will be chosen from the topics: numerical PDEs (iterative methods, multigrid, preconditioning), adaptive refinement, quadrature, eigenvalue problems (nuclear reactor criticality computations, power method, subspace iteration, AUTO), nonlinear equations and bifurcation (nonlinear thermal conduction, Newton-type methods), optimisation, networks and graphs (graph drawing, clustering, Google's pagerank algorithm).
Further information on programme availabilityProgramme availability:

MA40177 is Compulsory on the following programmes:

Department of Mathematical Sciences

MA40177 is Optional on the following programmes:

Department of Mathematical Sciences