CSC 4241 - GPU Programming

• Learn to program massively parallel processors using the CUDA programming API, tools, and techniques
• Design and develop algorithms that take advantage of highly parallel co-processors to solve technical and scientific problems
• Learn principles and patterns of parallel algorithms
• Learn NVIDIA GPU architecture features and constraints
• Understand data-parallel hardware in order to develop efficient algorithms
• Design numerical methods optimized for data parallel architectures
• Leverage data-parallel hardware to process large data sets common in modern big data applications
• Implement and analyze parallel algorithm patterns in the CUDA programming model
• Identify performance bottlenecks in parallel code
• Improve performance by applying common parallel techniques

• A working knowledge of the C/C++ programming language
• Familiarity with basic linear algebra

• Heterogeneous parallel computing
• GPU architecture
• Data parallelism
• CUDA programming structure
• Mapping threads to multidimensional data
• Memory access efficiency
• CUDA memory types
• Warps and SIMD hardware
• Floating-point data representation
• Numerical stability
• Parallel patterns

• Query of hardware resources and coding environment/tools
• Linear algebra operations such as vector addition, matrix multiplication
• Performance optimization leveraging shared and constant memory
• Image processing algorithms, such as image blur, color to grayscale conversion, convolution, stencil
• Using CUDA libraries
• Common parallel programming patterns
• Application case studies