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Course Outline
Introduction to Biren GPU Architecture
- Biren overview and use cases.
- Hardware layout: cores, memory, compute clusters.
- Comparison with NVIDIA and AMD GPUs.
Setting Up the Biren Programming Environment
- Installing the Biren SDK and runtime.
- Understanding the toolchain and compiler model.
- Basic project structure and build process.
GPU Programming with the Biren Stack
- Thread and block models.
- Memory management and data transfers.
- Kernel development and launch patterns.
Porting from CUDA to Biren
- Translation techniques for CUDA code.
- Common API mappings and adaptations.
- Code conversion labs and practice.
Debugging and Profiling
- Using Biren’s debugger and profiler.
- Identifying bottlenecks.
- Memory access patterns and optimisation.
Optimisation Techniques
- Thread scheduling and instruction pipelining.
- Loop unrolling and shared memory utilisation.
- Advanced kernel tuning for throughput.
Case Study and Application Examples
- Training a model with Biren accelerators.
- Porting and profiling a vision or NLP model.
- Comparing performance versus CUDA/NVIDIA.
Summary and Next Steps
Requirements
- An understanding of GPU architecture and parallel processing.
- Experience with CUDA, OpenCL, or similar GPU programming environments.
- Familiarity with deep learning frameworks such as PyTorch or TensorFlow.
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.
