Table 4: Summary of Course Information
1
2
1
2
3
4	3	1
5
6
7
8
PLO 1 PLO 2 PLO 3 PLO 4 PLO 5 PLO 6 PLO 7 PLO 8 PLO 9
PLO 10 PLO 11
√
√
√
√
C1C2C3A C3D
9
1
2
3
`
10
3
Name(s) of Academic 
Staff:
Dr. Muhammad Hanafi Yusop
Course Name: Parallel and Distributed Computing
Course Code: CAPC3010
Semester and Year 
offered:
Year Offered Semester Remarks:
Credit Value:	3
Course Classification:Major (core) 
Synopsis:
Parallel and Distributed Computing is a field of computer science that focuses on the use of multiple computing resources to solve computational problems. These resources 
can be multiple processors in a single computer (parallel computing) or multiple computers connected through a network (distributed computing). This field aims to improve 
computational speed, enhance performance, and provide scalable and reliable systems.
Pre-requisite/ co-
requisite (if any):
N/A
Course Learning 
Outcomes (CLO)
CLO1 Explain the techniques and methodologies of parallel and distributed computing. (C2, PLO1)
CLO2 Synthesize the performance of applications using parallel and distributed computing principles.(C3, PLO2)
CLO3 Employ core Computer Science methods and algorithms in creating efficient parallel applications. (P2, PLO3)
CLO4
Mapping of the Course Learning Outcomes to the Programme Learning Outcomes, Teaching Methods and Assessment Methods
Course Learning 
Outcomes
Programme Learning Outcomes (PLO)
Teaching Methods	Assessment Methods
Articulate the design, testing procedures, and performance evaluation of software systems, and effectively convey these concepts to others. (A3, PLO6)
CLO5
CLO6
CLO3	Lecture, Tutorial, Practical
Assignment
CLO4	Lecture, Tutorial, Practical
Assignment
CLO1	Lecture, Tutorial
Final Exam
CLO2	Lecture, Tutorial, Practical
Assignment, Final Exam,Test
CLO5
CLO6
C3E = Numeracy Skills, C3F = Leadership, Autonomy & Responsibility, C4A = Personal Skills, C4B = Entrepreneurial Skills, C5 = Ethics & Professionalism
Transferable Skills (if applicable)
(Skills learned in the course of study which 
can be useful and utilized in other settings)
Cognitive skills  
Digital Skills
Mapping with 
MQF Cluster of 
Learning 
Outcomes
Indicate the primary causal link between the CLO and PLO by ticking  '√' in the appropriate box.
C1 = Knowledge & Understanding, C2 = Cognitive Skills, C3A = Practical Skills, C3B = Interpersonal Skills, C3C = Communication Skills, C3D = Digital Skills,
Open-ended response (if any)
4
Distribution of Student Learning Time (SLT)
Note: This SLT calculation is designed for home grown programme only.

L T P O L T P O
1	2 1 1
2	2 1 1
3	2 1 1
4	2 1 1
5	2 1 1
6	2 1 1
7	2 1 1
8	2 1 1
9	2 1 1
10	2 1 1
11	2,32 1 1	2
12	2,32 1 1	2
13	4 2 1 1	2
14	4 2 1 1	2
15
16
NF2F
Independent Learning
(Asynchronous)
Physical
Online/ Technology-
mediated (Synchronous)
Introduction to Parallel and Distributed 
Computing
- Overview of Parallel and Distributed 
  Computing
- Motivation and Applications
- Types of Parallelism: Data vs. Task   
  Parallelism
- Distributed Systems Architecture
1	3
Course Content Outline and Subtopics CLO*
Learning and Teaching Activities**
Total SLT
Face-to-Face (F2F)
 - Motivation and Applications
- Types of Parallelism: Data vs. Task   
  Parallelism
- Distributed Systems Architecture
1	3
Parallel Computing Models and 
Architectures
- Flynn’s Taxonomy: SISD, SIMD, MISD, 
  MIMD
- Shared Memory vs. Distributed Memory
- Multicore and Manycore Processors
1	3
 Distributed Computing Models and 
Architectures
- Client-Server Model
- Peer-to-Peer Model
- Cloud Computing and Virtualization
2	3
 Distributed Algorithms
- Consensus and Agreement Protocols
- Leader Election Algorithms
- Mutual Exclusion in Distributed Systems
2	3
Performance Metrics and Parallel Algorithm 
Design
- Performance Metrics: Speedup, 
  Efficiency, Scalability
- Amdahl’s Law and Gustafson’s Law
- Parallel Algorithm Design Techniques
1	3
Parallel Programming Models
- Shared Memory Programming: 
  Pthreads, OpenMP
- Message Passing Interface (MPI)
- Data Parallel Programming: CUDA, 
  OpenCL
2	3
Scheduling and Load Balancing
- Task Scheduling in Parallel Systems
- Load Balancing Techniques
- Resource Allocation in Cloud 
   Computing
2,3	3
Security in Parallel and Distributed Systems
- Security Challenges in Distributed 
   Systems
- Cryptographic Protocols
- Access Control and Authentication
Advanced Topics
- Big Data and Analytics in Distributed  
   Systems
- Internet of Things (IoT) and Edge 
   Computing
Fault Tolerance and Reliability
- Fault Tolerance Techniques
- Replication and Consistency Models
- Checkpointing and Recovery
2	3
Distributed File Systems and Storage
- Distributed File Systems: HDFS, GFS
- NoSQL Databases: Cassandra, 
   MongoDB
- Data Replication and Consistency
2, 3	3
Future Trends in Parallel and 
   Distributed Computing
Course Review and Project Presentation
- Review of Key Concepts
- Discussion of Emerging Trends
- Student Project Presentations

17
18
19
20
1	10
2	4
3
4
5
1
2
3
4
5
B
C
C1
C2
61.67
Note: 
SUB-TOTAL SLT: 	94
Continous Assessement	%
Face-to-Face (F2F)
NF2F
Independent Learning for 
Assessment (Asynchronous)
Physical
Online/ Technology-
mediated (Synchronous)
Assignment	40%
Test	20	2
16
Final Assessement	%
Face-to-Face (F2F)
NF2F
Independent Learning for 
Assessment (Asynchronous)
Physical
Online/ Technology-
mediated (Synchronous)
SUB-TOTAL SLT: 
Final exam	40	2	8
10
SLT for Assessment:	26
GRAND TOTAL SLT:	120
A
% SLT for F2F Physical Component: 
[Total F2F Physical /(Total F2F Physical + Total F2F Online + Total Independent Learning) x 100)]
50.00
SUB-TOTAL SLT: 
% SLT for F2F Online Practical Component
[Total F2F Online Practical / (Total F2F Physical + Total F2F Online + Total Independent Learning) x 100]
Please  tick (√) if this course is Industrial Training/ Clinical Placement/ Practicum using 50% of Effective Learning Time (ELT)
* Indicate the CLO based on the CLO's numbering in Item 8
** For ODL programme: Courses with mandatory practical requiremnets imposed by the programme standards or any related standards can be exempted from complying to the minimum 80% ODL 
delivery rule in the SLT.
% SLT for Online & Independent Learning Component:
[(Total F2F Online + Total Independent Learning) /( Total F2F Physical + Total F2F Online + Total Independent Learning) x 100]
50.00
% SLT for All Practical Component:
[% F2F Physical Practical + % F2F Online Practical]
11.67
% SLT for F2F Physical Practical Component
[Total F2F Physical Practical /( Total F2F Physical + Total F2F Online + Total Independent Learning)  x 100)]
11.67
11
Identify special requirement or resources to deliver the 
course (e.g., software, nursery, computer lab, simulation 
room etc)

13 Other additional information (if applicable)
Note: Number of PLO indicated is purely for illustration purposes only and the number is subjected to the curriculum design.
12
References (include required and further readings, and 
should be the most current)
Rauber, T., & Rünger, G. (2024). Parallel programming: For multicore and cluster systems. Springer.
Steen, M. v., Tanenbaum, A. S. (2023). Distributed Systems. Netherlands: Amazon Digital Services LLC - Kdp.