Unit 1
Introduction to basic architecture and OS concepts –architecture of parallel computing–shared and distribution memory in parallel computing – parallel algorithm – performance metrices of parallel algorithm.
| Course Name | High Performance and Cloud Computing |
| Course Code | 23AID304 |
| Program | B.Tech in Artificial Intelligence and Data Science |
| Semester | 5 |
| Credits | 3 |
| Campus | Coimbatore , Amritapuri ,Faridabad , Bangaluru, Amaravati |
Introduction to basic architecture and OS concepts –architecture of parallel computing–shared and distribution memory in parallel computing – parallel algorithm – performance metrices of parallel algorithm.
Introduction to OpenMP – essentials of OpenMP – data sharing and synchronization – efficient OpenMP for matrix computing – Introduction to MPI and distributed memory parallel computing – communicating using MPI – Matrix representation of physical system and parallel matrix solvers – domain decomposition techniques
Overview of GPU architecture and its evolution –introduction to GPGPU and CUDA – CUDA programming –thread execution in CUDA programming – matrix computing in CUDA -introduction to cuBLAS and cuDNN libraries for linear algebra and deep learning – case studies of GPU: accelerated applications in scientific computing, data analytics, and machine learning
Introduction to cloud computing and its importance – benefits and challenges of cloud computing – types of cloud services (IaaS, PaaS, SaaS) and their characteristics – cloud computing architecture and its components – cloud storage and its types – cloud networking and its challenges – cloud security and its importance – cloud application – benefits and challenges of HPC and AI – synergy between HPC and AI – training and inference of AI models using HPC
Course Objectives
Course Outcomes
After completing this course, students will be able to
|
CO1 |
Apply high-performance computing in different research field. |
|
CO2 |
Design OpenMPI programme and CUDA programme |
|
CO3 |
Simulate on cloud computing system. |
|
CO4 |
Evaluate how the convergence of HPC and AI is transforming the data science. |
CO-PO Mapping
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PO/PSO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
PSO1 |
PSO2 |
PSO3 |
|
CO |
|||||||||||||||
|
CO1 |
3 |
2 |
– |
2 |
2 |
3 |
2 |
– |
– |
2 |
1 |
3 |
1 |
1 |
– |
|
CO2 |
2 |
3 |
3 |
3 |
3 |
– |
1 |
– |
2 |
1 |
2 |
3 |
3 |
3 |
1 |
|
CO3 |
2 |
2 |
3 |
2 |
3 |
1 |
1 |
– |
2 |
1 |
2 |
3 |
2 |
2 |
1 |
|
CO4 |
2 |
2 |
1 |
2 |
3 |
2 |
1 |
1 |
2 |
1 |
2 |
3 |
3 |
3 |
3 |
Evaluation Pattern
|
Assessment |
Internal/External |
Weightage (%) |
|
Assignments (Minimum 2) |
Internal |
20 |
|
Quiz(Minimum 2) |
Internal |
20 |
|
Mini Project |
Internal |
20 |
|
Mid-Term Examination |
Internal |
10 |
|
Term project/End semester examination |
External |
30 |
Text Books / References
“High Performance Computing: Modern Systems and Practices” by Thomas Sterling and Matthew Anderson
“CUDA by Example: An Introduction to General-Purpose GPU Programming” by Jason Sanders and Edward Kandrot
“Parallel Programming with MPI” by Peter S. Pacheco
Comer, D. (2021). The Cloud Computing Book: The Future of Computing Explained. CRC Press
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