Gracias por enviar su consulta! Uno de los miembros de nuestro equipo se pondrá en contacto con usted en breve.
Gracias por enviar su reserva! Uno de los miembros de nuestro equipo se pondrá en contacto con usted en breve.
Programa del Curso
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
Requerimientos
- 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 Horas
