This course introduces concepts, languages, techniques, and patterns for programming heterogeneous, massively parallel processors. Its contents and structure have been significantly revised based on the experience gained from its initial offering in 2012. It covers heterogeneous computing architectures, data-parallel programming models, techniques for memory bandwidth management, and parallel algorithm patterns.
- Week One: Introduction to Heterogeneous Computing, Overview of CUDA C, and Kernel-Based Parallel Programming, with lab tour and programming assignment of vector addition in CUDA C.
- Week Two: Memory Model for Locality, Tiling for Conserving Memory Bandwidth, Handling Boundary Conditions, and Performance Considerations, with programming assignment of simple matrix-matrix multiplication in CUDA C.
- Week Three: Parallel Convolution Pattern, with programming assignment of tiled matrix-matrix multiplication in CUDA C.
- Week Four: Parallel Scan Pattern, with programming assignment of parallel convolution in CUDA C.
- Week Five: Parallel Histogram Pattern and Atomic Operations, with programming assignment of parallel scan in CUDA C.
- Week Six: Data Transfer and Task Parallelism, with programming assignment of parallel histogram in CUDA C.
- Week Seven: Introduction to OpenCL, Introduction to C++AMP, Introduction to OpenACC, with programming assignment of vector addition using streams in CUDA C.
- Week Eight: Course Summary, Other Related Programming Models –Thrust, Bolt, and CUDA FORTRAN, with programming assignment of simple matrix-matrix multiplication in choice of OpenCL, C++AMP, or OpenACC.
- Week Nine: complete any remaining lab assignments, with optional, bonus programming assignments in choice of OpenCL, C++AMP, or OpenACC.
- Wen-mei Hwu - Department of Electrical and Computer Engineering
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算法设计与分析 Design and Analysis of Algorithms