Parallel Computing Course Outline
Module 1: Introduction to Parallel Computing
Week 1: Overview of Parallel Computing
Definition and History of Parallel Computing
Applications of Parallel Computing
Advantages and Challenges of Parallel Computing
Week 2: Fundamental Concepts
Parallelism vs. Concurrency
Types of Parallelism: Data Parallelism, Task Parallelism
Levels of Parallelism: Bit-level, Instruction-level, Data-level, Task-level
Flynn’s Taxonomy: SISD, SIMD, MISD, MIMD
Module 2: Parallel Computer Architectures
Week 3: Parallel Architectures
Shared Memory Architecture
Distributed Memory Architecture
Hybrid Architectures
Week 4: Memory and Communication
Memory Hierarchies
Cache Coherence and Memory Consistency
Interconnection Networks: Topologies and Properties
Communication Models: Synchronous and Asynchronous
Module 3: Parallel Programming Models
Week 5: Parallel Programming Basics
Principles of Parallel Algorithm Design
Decomposition Techniques
Load Balancing and Scheduling
Week 6: Parallel Programming Models
Shared Memory Programming: Threads, OpenMP
Distributed Memory Programming: MPI
Hybrid Programming: Combining OpenMP and MPI
Module 4: Parallel Algorithms and Performance
Week 7: Parallel Algorithms
Sorting Algorithms: Parallel Merge Sort, Parallel Quick Sort
Matrix Operations: Matrix Multiplication, Matrix Decomposition
Graph Algorithms: Parallel BFS, Parallel Dijkstra’s Algorithm
Week 8: Performance Metrics and Optimization
Speedup and Efficiency
Scalability: Strong Scaling, Weak Scaling
Amdahl’s Law and Gustafson’s Law
Performance Bottlenecks and Optimization Techniques
Module 5: Advanced Topics in Parallel Computing
Week 9: Heterogeneous Computing
GPUs and Accelerators
CUDA Programming
OpenCL Programming
Week 10: Parallel Computing in the Cloud
Cloud Computing Paradigms
Parallel Programming in the Cloud
Case Studies and Applications
Week 11: Fault Tolerance and Reliability
Fault Tolerance Techniques
Checkpointing and Recovery
Reliability in Large-scale Systems
Week 12: Emerging Trends and Future Directions
Quantum Computing
Neuromorphic Computing
Parallel Computing for AI and Machine Learning
Future Directions in Parallel Computing
Module 6: Practical Applications and Case Studies
Week 13: Case Studies in Scientific Computing
Parallel Computing in Physics
Parallel Computing in Chemistry
Parallel Computing in Biology
Week 14: Parallel Computing in Industry
Big Data Analytics
Financial Modeling
Real-time Systems and Simulations
Week 15: Course Review and Final Project
Review of Key Concepts
Final Project Presentations and Discussions
Course Wrap-up and Future Learning Paths