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.