Short Title:Scalable Computing
Full Title:Scalable Computing
Language of Instruction:English
Module Code:COMP H6003
 
Credits: 10
Field of Study:Computing
Module Delivered in 4 programme(s)
Reviewed By:FINBARR FEENEY
Module Author:JOHN BURNS
Module Description:This module introduces the following parallel computing topics - Performance Metrics for Parallel Systems, Effect of Granularity on Performance, Scalability of Parallel Systems, Parallel Programming for Big Data
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Successfully partition computationally intensive data tasks into n-parallel GPU-based components/ CPU components/ FPGA where appropriate and to appraise performance improvement using on CPU and off CPU techniques
LO2 Integrate advanced theoretical knowledge and solve complex problems in Big Data Analytics
LO3 Develop and solve analytical models of parallel systems
LO4 Critically assess the performance and implementation tradeoffs of parallel computing platforms
LO5 Assess and solve complexity analysis of serial and parallel algorithms
 

Module Content & Assessment

Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Continuous Assessment CA1 – elapsed, individual 1 25.00 n/a
Continuous Assessment CA2 – elapsed, individual 2,3 25.00 n/a
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam End-of-Semester Final Examination 1,2,3,4,5 50.00 End-of-Semester

TU Dublin – Tallaght Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 2.00 Every Week 2.00
Lab No Description 1.00 Every Week 1.00
Total Weekly Learner Workload 3.00
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 2.00 Every Week 2.00
Lab No Description 1.00 Every Week 1.00
Total Weekly Learner Workload 3.00
Total Weekly Contact Hours 3.00
 

Module Resources

Required Book Resources
  • Packt 2016, Big Data Analytics with R, Packt Publishing (29 July 2016) [ISBN: 9781786466457]
Recommended Book Resources
  • Ananth Grama Anshul Gupta George Karypis Vipin Kumar 2003, Introduction to Parallel Computing, Addison Wesley [ISBN: 0201648652]
This module does not have any article/paper resources
This module does not have any other resources
 

Module Delivered in

Programme Code Programme Semester Delivery
TA_KDMCO_M M. Sc. in Distributed and Mobile Computing 1 Elective
TA_KITMG_M M.Sc. in Information Technology Management 1 Elective
TA_KDMCO_PD Postgraduate Diploma in Distributed and Mobile Computing 1 Elective
TA_KITMG_PD Postgraduate Diploma in Information Technology Management 1 Elective