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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 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 optimization
Optimization Techniques
- Thread scheduling and instruction pipelining
- Loop unrolling and shared memory use
- 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 vs 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 optimization specialists
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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Testimonials (1)
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.
