Teaching Responsibility

LJMU Schools involved in Delivery:

Computer Science and Mathematics

Learning Methods

Lecture

Practical

Module Offerings

7506BDSA-JAN-PAR

Aims

The course provides students with a comprehensive knowledge about the principles of high performance computing (HPC) to handle big data considering their five dimensions known as the 5Vs (Volume, Variety, Velocity, Variability, and Veracity). It covers distributed data processing with Hadoop and MapReduce, the surveys and architectures of cloud computing platforms, the scaling data science techniques and algorithms, and the performance enhancement by calculating the number of processors needed to perform a particular task. The companion practical sessions of the course focus on writing programs and building a node-compute-cluster.

Learning Outcomes

1.
Demonstrate comprehensive knowledge in HPC.
2.
Conduct research activities in several areas related to HPC involving parallel programming, cloud computing and cluster computing.
3.
Implement advanced skills for enhancing computer performance.
4.
Identify and formulate practical problems in Distributed data processing.
5.
Apply advanced software to acquire practical experience in writing concurrent programs and building node computer cluster.

Module Content

Outline Syllabus:Introduction to HPC Big Data 5Vs Distributed Data Processing with Hadoop & MapReduce Survey & Architectures of Cloud Computing Platforms Scaling Data Science Techniques & Algorithms Performance Enhancement Flynn's taxonomy SISD, MISD,SIMD, MIMD Vector Computing Parallel Programming Cluster Computing Types of Super Computers
Additional Information:The module contributes to the master’s aim to equip the student with the required abilities and skills to perform data science on real-world applications.

Assessments

Exam

Report

Report