NDAK14009U Parallel Functional Programming (PFP)

Volume 2020/2021

Data-parallel programming models express parallelism declaratively (explicitly) by means of higher-order language constructs, whose rich semantics allow high-level reasoning for exploring the large space of strategies for efficiently mapping application's parallelism to hardware.  

The aim of the course is to introduce the principles and practice of parallel programming (i.e., programming using multiple hardware cores or processors in order to gain speed) in a declarative programming setting. The course covers both multi-core parallel programming (for MIMD CPU programming) and many-core parallel programming (as for SIMD GPGPU programming).   

The course includes current research on these topics and relies heavily on scientific papers as its source materials. The course will demonstrate the presented parallelisation strategies on applications from the machine-learning, image-processing and finance domains. 

The lectures will provide an overview of approaches to parallel programming and give practical instructions to writing, testing, and optimising data-parallel programs. The topics covered in the lecture will be exercised in lab assignments, consisting of programming and analysis of programs as well as questions for theoretical discussion.

Learning Outcome

Knowledge of

  • the difference between the concepts of concurrency and parallelism, and between data parallelism and task parallelism
  • well-known parallelisation strategies, programming patterns, and program skeletons
  • different approaches to parallelism taken in various languages, with particular focus on how high-level description of parallelism may be mapped in a principled way to high-performance hardware.

Skills to

  • express a parallel computation in data-parallel paradigms
  • write, modify, and test data-parallel programs, in different programming environments, targeting different architectures such as multi-core CPUs and GPGPUs


Competences to

  • identify opportunities for using data-parallel programming to parallelise algorithms
  • select a suitable programming language/dialect to implement a parallel algorithm on a given hardware platform

The course does not use a single textbook but instead provides tutorials and scientific papers available from the course pages.

The course syllabus assumes basic knowledge and programming competences in a functional programming language, which, at DIKU, can be acquired through ”Advanced programming”, or through self-study.

Academic qualifications equivalent to a BSc degree is recommended.
Lectures, in-class exercises, group work on programming and analysis assignments.
The course material is intended to present synergies with the material of Programming Massively-Parallel Hardware (PMPH) and Large-Scale Data Analysis courses. This means that there are connecting elements with said courses, but they are not pre-requisites for taking this course.
  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 15
  • Exercises
  • 60
  • Laboratory
  • 14
  • Project work
  • 83
  • Exam Preparation
  • 5
  • Exam
  • 1
  • Total
  • 206
Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Continuous assessment
Continuous assessment based on 3-4 individual assignments and a group mini-project with individual oral defense (20-25 minutes).

The part-examinations must be individually approved. The final grade is based on an overall assessment.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

Resubmission of assignments and mini-project and a 30 minutes oral examination without preparation in full course syllabus.

The part-examinations must be individually approved. The final grade is based on an overall assessment.

Criteria for exam assesment

See Learning Outcome.