B.E Artificial Intelligence and Machine Learning High Performance Computing Syllabus - Mumbai University
This is the Third Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2022-23. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.
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Syllabus for High Performance Computing
Module 0: Prerequisite
- Computer Organization, C Programming, Data structures and Algorithm Analysis.
Module I: Introduction
- Introduction to Parallel Computing: Motivating Parallelism, Scope of Parallel Computing, Levels of parallelism (instruction, transaction, task, thread, memory, function), Models (SIMD, MIMD, SIMT, SPMD, Dataflow Models, Demand driven Computation). Self-learning Topics: Parallel Architectures: Interconnection network, Processor Array, Multiprocessor.
Module II: Parallel Programming Platforms
- Parallel Programming Platforms: Implicit Parallelism: Dichotomy of Parallel Computing Platforms, Physical Organization of Parallel Platforms, Communication Costs in Parallel Machines. Self-learning Topics: Trends in Microprocessor & Architectures, Limitations of Memory System Performance.
III Parallel Algorithm And Concurrency
- Principles of Parallel Algorithm Design: Preliminaries, Decomposition Techniques, Characteristics of Tasks and Interactions, Mapping Techniques for Load Balancing, Basic Communication operations: Broadcast and Reduction Communication types. Self-learning Topics: Parallel Algorithm Models
IV Performance Measures for HPC
- Performance Measures : Speedup, execution time, efficiency, cost, scalability, Effect of granularity on performance, Scalability of Parallel Systems, Amdahl‘s Law, Gustavson‘s Law. Self-learning Topics: Performance Bottlenecks.
Module V: Programming Paradigms for HPC
- Programming Using the Message-Passing Paradigm : Principles of Message Passing Programming, The Building Blocks: Send and Receive Operations, MPI: the Message Passing Interface, Topology and Embedding. Parallel Algorithms and Applications : One-Dimensional Matrix-Vector Multiplication, Graph Algorithms, Sample Sort, Two-Dimensional MatrixVector Multiplication. Self-learning Topics: Introduction to OpenMP.
Module VI: General Purpose Graphics Processing Unit(GPGPU) Architectureand Programming
- OpenCL Device Architectures, Introduction to OpenCL Programming. Self-learning Topics: Introduction to CUDA architecture, and Introduction to CUDA Programming.
Text Books
- 1 AnanthGrama, Anshul Gupta, George Karypis, Vipin Kumar , ―Introduction to Parallel Computing‖, Pearson Education, Second Edition, 2007.
- 2 Kai Hwang, Naresh Jotwani, ―Advanced Computer Architecture: Parallelism, Scalability, Programmability‖, McGraw Hill, Second Edition, 2010.
- 3 Edward Kandrot and Jason Sanders, ―CUDA by Example – An Introduction to General Purpose GPU Programming‖, Addison-Wesley Professional ©, 2010.
- 4 Georg Hager, Gerhard Wellein, ―Introduction to High Performance Computing for Scientists and Engineers", Chapman & Hall / CRC Computational Science series, 2011.
- 5 Benedict Gaster, Lee Howes, David Kaeli, Perhaad Mistry, Dana Schaa , ―Heterogeneous Computing with OpenCL‖ , 2nd Edition, Elsevier, 2012.
- 1 Michael J. Quinn, ―Parallel Programming in C with MPI and OpenMP‖, McGraw-Hill International Editions, Computer Science Series, 2008.
- 2 Kai Hwang, Zhiwei Xu, ―Scalable Parallel Computing: Technology, Architecture, Programming‖, McGraw Hill, 1998.
- 3 Laurence T. Yang, MinyiGuo, ―High- Performance Computing: Paradigm and Infrastructure‖ Wiley, 2006.
- 4 Fayez Gebali, ―Algorithms and Parallel Computing‖, John Wiley & Sons, Inc., 2011.
Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2022-23. Wording is as printed in that syllabus. Module numbering is as printed there too.
The complete syllabus
This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.