This module is offered in 2020-21.

Aims

The aims of this module are:

  • To introduce high-level pattern-based approaches to programming multicore systems.
  • To explain how high-level patterns patterns link to parallel hardware implementations.
  • To provide experience with important parallel models: task and data-parallelism, MIMD and SIMD implementations etc.

Learning Outcomes

On successful completion of this module, the student should:

  • Be aware of the need for abstraction in parallel programming.
  • Be familiar with a variety of common high-level patterns of parallel programming.
  • Have a working knowledge of the implementation of those patterns.
  • Understand performance and debugging issues on both CPUs and special-purpose parallel hardware.

Syllabus

  • Basic parallel constructs.
  • Patterns of parallelism: divide-and-conquer, map-reduce, task farms, pipelines, etc.
  • Task and data parallelism.
  • Implementation of parallel patterns through concrete skeletons.
  • Parallel memory management and other runtime issues.
  • Special-purpose hardware: programming SIMD algorithms on GPU hardware.

Compulsory Elements

This module has no compulsory elements beyond those common to all modules (mark of 4 in each assessment component).

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Last Published: 19 Oct 2020.