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Course Outline
Introduction to Biren GPU Architecture
- Overview of Biren and its primary use cases
- Hardware layout: cores, memory, and compute clusters
- Comparative analysis with NVIDIA and AMD GPUs
Establishing the Biren Programming Environment
- Installation of the Biren SDK and runtime components
- Navigating the toolchain and compiler model
- Fundamental project structures and build workflows
GPU Programming with the Biren Stack
- Thread and block models
- Memory management and data transfer mechanisms
- Kernel development and launch patterns
Transitioning from CUDA to Biren
- Techniques for translating CUDA code
- Mapping common APIs and necessary adaptations
- Hands-on labs and practice in code conversion
Debugging and Profiling
- Utilising Biren’s debugger and profiler tools
- Identifying and analysing performance bottlenecks
- Optimising memory access patterns
Advanced Optimisation Techniques
- Thread scheduling and instruction pipelining
- Loop unrolling and efficient shared memory usage
- Advanced kernel tuning for maximum throughput
Case Studies and Application Examples
- Training models using Biren accelerators
- Porting and profiling vision or NLP models
- Performance benchmarking against CUDA/NVIDIA platforms
Summary and Future Directions
Requirements
- A solid grasp of GPU architecture and parallel processing concepts
- Practical experience with CUDA, OpenCL, or comparable GPU programming environments
- Working knowledge of deep learning frameworks such as PyTorch or TensorFlow
Target Audience
- HPC developers
- AI infrastructure engineers
- Performance optimisation specialists
21 Hours
Testimonials (2)
The extensive selection of tools presented
Miruna Buzduga - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.