Module Code: H9SCP
Long Title Scalable Cloud Programming
Title Scalable Cloud Programming
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 10
Module Coordinator: Horacio Gonzalez-Velez
Module Author: Noel Cosgrave
Specifications of the qualifications and experience required of staff

MSc and/or PhD degree in computer science or cognate discipline.  Experience lecturing in the field. May have industry experience also.

Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Identify and critically evaluate functional and non-functional characteristics of parallel workloads on cloud platforms
LO2 Analyse sequential programs to identify suitable candidates for parallelisation.
LO3 Demonstrate competence in writing parallel programs using scalable algorithms and techniques
LO4 Recognise and describe techniques and tools to improve the productivity of parallel programming on emerging computing architectures
LO5 Identify and critically evaluate system-specific levels of parallelism and co-scheduling of computation for scalability and resilience.
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).

No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

Internal to the programme


Module Content & Assessment

Indicative Content
Parallel Programming Preliminaries
Sequential Algorithms vs. Parallel algorithms. Parallelism vs Concurrency. Process Management. Multitasking.
Parallel and Distributed Software
Threads. Coordinating Process and Threads. Shared-Memory. Distributed Memory. Programming Hybrid Systems.
Scaling Deployments
Paradigms of Parallel Computing in the Cloud. SPMD and HPC-style parallelism. Many-Task Parallelism.
Parallel Software Patterns
Data- vs. Process Parallelism. Data patterns: Map, reduce, scan, gather. Collectives. Task Farms and Pipelines.
MapReduce and Graph Data-flows. Recursive and workflow systems for MapReduce.
MapReduce Cost Models
Complexity and cost models for MapReduce with emphasis on communication costs and task networks.
Multi-stage and data-flow computing
Resilient Distributed Data Sets (RDDs). RDDs vs DAG Tasks.
Streaming Data Model
Stream sources, stream queries, and processing. Sampling data.
Stream Operations
Filtering, counting, combining and estimating.
Stream Processing
Building complex pipelines and models
Cloud Performance
Metrics and Benchmarks. Autoscaling, Scale-Out, Scale-up and Mixed Scaling. Scaling Strategies.
Using Scalable Services
Deploying concurrent stream processing and batch processing pipelines
Assessment Breakdown%
End of Module Assessment50.00%


Full Time

Assessment Type: Project % of total: 50
Assessment Date: n/a Outcome addressed: 3,4,5
Non-Marked: No
Assessment Description:
Develop a complex scalable cloud computing solution, which should be informed by a review of recent work in the domain, and should be submitted in the form of a conference-style report The working solution will be demonstrated to the lecturer, either by means of a project video or in-class presentation. Marked elements include the methodology, implementation, clarity of presentation and depth of understanding of the work carried out and its broader implications.
Assessment Type: Written (0080) % of total: 50
Assessment Date: n/a Outcome addressed: 1,2,3
Non-Marked: No
Assessment Description:
The test will assess learners’ knowledge and understanding of data and computing architectures, programming models, and storage concepts. A sample question, marking scheme, and solution, is provided in Appendices.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
Reassessment Description
Reassessment of this module will be via proctored examination.

NCIRL reserves the right to alter the nature and timings of assessment


Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 36 Per Semester 3.00
Tutorial No Description 24 Per Semester 2.00
Independent Learning No Description 190 Per Semester 15.83
Total Weekly Contact Hours 5.00

Module Resources

Recommended Book Resources
  • Ian Foster,Dennis B. Gannon. (2017), Cloud Computing for Science and Engineering, MIT Press, p.392, [ISBN: 978-0-262-03724-2].
Supplementary Book Resources
  • Peter Pacheco. (2019), An Introduction to Parallel Programming, 2nd Edition. Morgan Kaufmann, Amsterdam, [ISBN: 0128046058].
  • Kai Hwang. (2017), Cloud Computing for Machine Learning and Cognitive Applications, MIT Press, Cambridge, MA., [ISBN: 026203641X].
  • K.C. Wang. (2018), Systems Programming in Unix/Linux, Springer, [ISBN: 978-3-319-92428-1].
  • H Karau et al.. (2015), Learning Spark, 1st edition. O'Reilly Media, [ISBN: 1449358624].
  • Tom White. (2015), Hadoop: The Definitive Guide, 4th Edition. O'Reilly Media, [ISBN: 1449311520].
  • Maurice Herlihy,Nir Shavit. (2012), The Art of Multiprocessor Programming, Revised Edition. Morgan Kaufmann, Amsterdam, [ISBN: 0123973376].
  • William Gropp,Ewing Lusk,Anthony Skjellum. (2015), Using MPI: Portable Parallel Programming with the Message-Passing Interface (Scientific and Engineering Computation), 3rd Edition. MIT Press, Cambridge, MA, [ISBN: 0262527391].
  • Ruud van der Pas,Eric Stotzer,Christian Terboven. (2017), Using OpenMP: The Next Step: Affinity, Accelerators, Tasking, and SIMD (Scientific and Engineering Computation), 1st Edition. MIT Press, Cambridge, MA, [ISBN: 0262534789].
  • Joanna Kołodziej (Editor), Horacio González-Vélez (Editor). (2019), High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet, 1. Springer, Cham, [ISBN: 3030162710].
Recommended Article/Paper Resources
  • R. Buyya et al.. (2019), Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade., ACM Computing Surveys, 51(5), [ISSN: 0360-0300].
  • J. Dean, S. Ghemawat. (2010), MapReduce: a flexible data processing tool., Communications of the ACM, 53(1), [ISSN: 0001-0782].
  • H. González-Vélez, M. Leyton. (2010), A survey of algorithmic skeleton frameworks: high-level structured parallel programming enablers, Software: Practice and Experience, 40(12), [ISSN: 0038-0644].
  • N. P. Jouppi et al.. (2017), In-datacenter performance analysis of a tensor processing unit.
This module does not have any other resources
Discussion Note: