CUDA is NVIDIA's Supercomputing Architecture which is beating Intel in Supercomputing Segment. Intel is still developing such kind of Architecture larabee. It is the hottest Field to be possessed in 2010 according to NVIDIA's CEO Jensun Huanag.

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Course Overview:

This course (HBCS102) is divided into three modules: Level "A" , Level "B" and Level "C", level C being the most advance course, comprising mainly of practical applications.

Level "A" is an introductory course on parallel programming with about only 20% of the time devoted for CUDA programming. This level does not require any parallel computing knowledge. Only a Data structures level course is required.

Level "B" discusses parallel programming concepts in detail giving specific focus on CUDA programming.  Specifically you are exposed to the following special topics: Performance metrics - speedup, utilization, efficiency, scalability, Models of Parallel Computation: SIMD  (Single Instruction Multiple Data), MIMD (Multiple Instruction Multiple Data), GPU Compute Architecture, CUDA, Memory organization in CUDA, Memory Optimization,  Coalesced Access, Occupancy, Transparent Scalability, Performance Guidelines, Fast Matrix Multiplication.

Level "C" is the advance course and is mainly related to practical implementations. Level C is a hands-on course involving significant parallel programming on massive-core GPUs fundamentally CUDA compatible NVIDIA's GPU. Specifically we will be working on NIDS (Network Intrusion Detection System) acceleration on GPUs. This will require core knowledge of networking fundamentals as well CUDA programming skills.

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Target Audiences:

Second, Third year and final year students of Computer Science, Electronics & Comm Engineering, and Electronics & Instrumentation can enroll for this course.

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Prerequisites:

For Level "A", the student should be familiar with the concepts of C programming language. Although the parallel programming will be taught in the training in Level "A", but some exposure to it will help you grasp the concept quickly.   
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Reference Books

Introduction to Algorithms, Third Edition
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein

Introduction to Parallel Computing by Ananth Grama, George Karypis, Vipin Kumar and Anshul Gupta (Pearson)

CUDA programming Guide, CUDA Best Practice Guide  (Download from nvidia.com)

GPU GEMS 3 by Hubert Nguyen 
Reading Material from Internet 

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