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Course Outline
Introduction to Biren GPU Architecture
- Overview of Biren and its key use cases
- Hardware layout: cores, memory, and compute clusters
- Comparative analysis with NVIDIA and AMD GPUs
Setting Up the Biren Programming Environment
- Installation of the Biren SDK and runtime
- Understanding the toolchain and compiler model
- Basic project structure and the build process
GPU Programming with the Biren Stack
- Thread and block execution models
- Memory management and data transfer strategies
- Kernel development and launch patterns
Porting from CUDA to Biren
- Techniques for translating CUDA code
- Common API mappings and necessary adaptations
- Practical labs and exercises for code conversion
Debugging and Profiling
- Utilisation of Biren's debugger and profiler tools
- Identifying performance bottlenecks
- Analysing memory access patterns and applying optimisations
Optimisation Techniques
- Thread scheduling and instruction pipelining
- Loop unrolling and efficient use of shared memory
- Advanced kernel tuning for maximum throughput
Case Study and Application Examples
- Training a model using Biren accelerators
- Porting and profiling vision or NLP models
- Comparing performance against CUDA and NVIDIA platforms
Summary and Next Steps
Requirements
- A solid understanding of GPU architecture and parallel processing concepts
- Practical experience with CUDA, OpenCL, or comparable GPU programming environments
- Familiarity with 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.
