Introductory description

The module will address the increased use of computer simulation and data analysis on high performance computers in all fields of computational physics and other sciences. Computing skills are greatly valued across science and beyond and we encourage students to go as far as they can to develop such skills.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Programming for efficiency. Modern cache architectures and CPU pipelining. Avoiding expensive and repeated operations. Compiler optimisation flags. Profiling with gprof.

Introduction to parallel computing. Modern HPC hardware and parallelisation strategies. Applications in Physics, super problems need super-computers.

Shared memory programming. The OpenMP standard. Parallelisation using compiler directives. Threading and variable types. Loop and sections constructs. Program correctness and reproducibility. Scheduling and false sharing as factors influencing performance.

Distributed memory programming. The MPI standard for message passing. Point-to-point and collective communication. Synchronous vs asynchronous communication. MPI communicators and topologies.

GPU programming. CUDA vs OpenCL. Kernels and host-device communication. Shared and constant memory, synchronicity and performance. GPU coding restrictions.

Limitations to parallel performance. Strong vs weak scaling. Amdahl’s law. Network contention in modern many-core architectures. Mixed mode OpenMP+MPI programming.

Learning outcomes

By the end of the module, students should be able to:

• Use visualisation libraries and desktop visualisation tools; be able to write a scientific application with a graphical user interface, using a toolkit
• Write a parallel program using shared-memory or message passing constructs, for physics applications
• Explain the concepts underlying Grid computing and e-science, and discuss their general areas of applicability.

Indicative reading list

R Chandra et al, Parallel Programming in OpenMP (Morgan Kaufmann, 2000)
P Pacheco, Parallel Programming with MPI (Morgan Kaufmann, 1996)
M Quinn, Parallel Programming in C with MPI and OpenMP (McGraw-Hill, 2003)
D Kirk and W Hwu, Programming Massively Parallel Processors (Elsevier, 2010)

View reading list on Talis Aspire

Subject specific skills

Skills in computer-modelling, reasoning, thinking. Knowledge of mathematics and physics

Transferable skills

Analytical, communication, computing and general IT, problem-solving, self-study