CS402-15 High Performance Computing

Introductory description

The module provides a solid foundation in High Performance Computing (HPC) and its role in science and engineering.

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.

• Fundamental concepts in High Performance Computing.
• Shared memory programming (OpenMP).
• Message passing programming (MPI).
• GPU programming.
• Parallel decomposition.
• Performance measurement and analysis.
• High performance I/O.
• High performance networking.
• High Performance Computing systems.
• Typical scientific applications.

Learning outcomes

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

• Understand the role of HPC in science and engineering.
• Be familiar with popular parallel programming paradigms.
• Understand commonly used HPC platforms with particular reference to Cluster system.
• Understand the means by which to measure, assess and analyse the performance of HPC applications.
• Understand the role of administration, workload and resource management in an HPC management software.
• Understand the mechanisms for evaluating the suitability of different HPC solutions to solving scientific problems.

Indicative reading list

Suggested Reading:
Peter Pacheco, Introduction to Parallel Programming, Morgan Kaufmann Publishers, 2011;
Michael J. Quinn, Parallel programming in C with MPI and OpenMP, McGraw-Hill Higher Education, 2004;
William Gropp, Using MPI: portable parallel programming with the message-passing interface, MIT press, 1999;
Further reading: Introduce the Graph 500;
Further Reading: A Note on the Zipf Distribution of Top500 Supercomputers;
Further Reading: Vectorizing C Compilers - How Good Are They?;
Further Reading: Further Reading in High Performance Compilers for Parallel Computing;

Subject specific skills

analytical skills by applying the HPC knowledge learned in this module to develop HPC applications and analyzing their performance, mathmatical thinking skills by linking rigor in performance modelling with the design of parallelization strategies, problem solving and IT skills by applying the learned knowledge to do practical lab sesssions and the courseworks; presentation and communication skills by writing the report of presenting the practical work conducted in the courseworks and discussing the experimental results; critical thinking skills by analyzing and comparing the pros and cons of different HPC solutions.

Transferable skills

Communication and presentation skills