School of Computer Science

Module 06-22755 (2013)

Parallel Programming (Extended)

Level 4/M

Dan Ghica Semester 2 10 credits
Co-ordinator: Dan Ghica
Reviewer: Uday Reddy

The Module Description is a strict subset of this Syllabus Page.


This module covers the programming of massively parallel architectures such as graphics processing units (GPUs), multi-cores and field-programmable gate arrays (FPGAs). The module is focussed on the architecture of such devices and their use in speeding-up common computational tasks. Recent developments in such architectures made this task substantially easier and newly available programming tools allow for high-level straightforward programming of these systems. The module will provide a largely practical introduction to the topic. Lectures will address the basic architectural principles of modern parallel architectures and relevant algorithms and programming techniques. Students will be expected to develop a research-informed critical approach to understanding parallel architectures and techniques.


The aims of this module are to:

  • understand GPU architecture
  • understand what applications areas are most suitable to parallel programming
  • practical understanding of parallel algorithms
  • practical understanding of parallel programming techniques
  • concrete skills in programming Nvidia GPUs using the CUDA framework
  • skills in using the software tools provided by the CUDA framework

Learning Outcomes

On successful completion of this module, the student should be able to:

1 describe and explain modern parallel architectures
2 describe and explain applications of parallel programming
3 describe and explain parallel programming models
4 design simple parallel algorithms
5 implement more advanced parallel algorithms
6 demonstrate a research-informed critical understanding of parallel architectures and parallelization techniques


Some familiarity with the Haskell programming language would be helpful.

Taught with

Cannot be taken with

Teaching methods

2 hrs/week lectures; 1 hr/week practical sessions

Contact Hours: 34


Sessional: 1.5 hr examination (80%), continuous assessment (20%). Both the examination and the continuous assessment are internal hurdles: students must pass both in order to pass the module.

Supplementary (where allowed): 1.5 hr examination (80%) with the continuous assessment mark carried forward (20%)

Detailed Syllabus

  1. Contrasting CPU and GPU architectures
  2. The CUDA programming model
  3. A first example: matrix multiplication
  4. The CUDA memory model
  5. GPU as part of the PC architecture
  6. Detailed threading model
  7. Detailed memory model
  8. Control flow on the GPU
  9. Floating-point aspects
  10. Parallel programming concepts
  11. Parallel algorithms
  12. Reduction
  13. Advanced features

Programmes containing this module