Welcome to the Heterogeneous Computing curriculum repository. This open-source project is designed to provide a comprehensive learning path for students and professionals looking to master heterogeneous computing systems. The curriculum covers foundational concepts, parallel programming, GPU and FPGA programming, performance optimization, AI integration, systems design, and culminates in a capstone project.
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Foundations of Heterogeneous Computing (4 weeks)
Establish a solid understanding of heterogeneous computing architectures and their applications. -
Parallel Computing and Multithreading (4 weeks)
Learn the principles of parallel computing and develop multithreaded applications. -
GPU Programming and CUDA (4 weeks)
Master GPU programming using CUDA to accelerate compute-intensive tasks. -
FPGA and Reconfigurable Computing (4 weeks)
Dive into FPGA programming and reconfigurable computing for specialized applications. -
Performance Optimization and Profiling (3 weeks)
Optimize and profile heterogeneous computing applications for maximum performance. -
Heterogeneous Computing in AI and Machine Learning (3 weeks)
Integrate heterogeneous computing architectures into AI and machine learning workflows. -
Heterogeneous Systems Design and Integration (3 weeks)
Design and integrate heterogeneous computing systems for optimal performance and scalability. -
Capstone Project (4 weeks)
Apply all learned skills to develop a comprehensive heterogeneous computing project.
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Hardware:
- CPU with multi-core support
- NVIDIA GPU with CUDA support
- FPGA development board (e.g., Xilinx or Intel)
- Development boards (e.g., Raspberry Pi)
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Software:
- CUDA Toolkit
- FPGA development tools (e.g., Xilinx Vivado)
- Programming languages: C/C++, Python
- Git and GitHub account
- Clone the repository:
git clone https://github.com/yourusername/heterogeneous-computing-curriculum.git cd heterogeneous-computing-curriculum