1. Review of Traditional Computer Architecture – Basic five stage RISC Pipeline, Cache Memory, Register
2. SIMD instructions
3. GPU architectures - Streaming Multi Processors, Cache Hierarchy, The Graphics Pipeline
4. Introduction to CUDA programming
5. Multi-dimensional mapping of dataspace, Synchronization
6. Warp Scheduling, Divergence
7. Memory Access Coalescing
8. Optimization examples : optimizing Reduction Kernels
9. Optimization examples : Kernel Fusion, Thread and Block
10. OpenCL basics
11. OpenCL for Heterogeneous Computing
12. Application Design : Efficient Neural Network Training/Inferencing

Enable students to utilize GPU architectures.

Train students for designing hardware and software for GPU computing.

Students will have basic knowledge of GPU architecture and programming.

John L.Hennessy and David A. Patterson, Computer Architecture A Quantitative Approach, Elsevier

David Kirk and Wen-mei Hwu, Programming Massively Parallel Processors, Elsevier

Benedict Gaster,Lee Howes, David R. Kaeli, Heterogeneous Computing with OpenCL, Elsevier

Jason Sanders, Edward Kandrot, CUDA by Example: An Introduction to General-Purpose GPU Programming, NVDIA