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Duration 21 hours
Course Outline
Introduction to Biren GPU Architecture
- Overview of Biren technology and key use cases.
- Hardware composition: cores, memory structure, and compute clusters.
- Comparative analysis with NVIDIA and AMD GPUs.
Establishing the Biren Programming Environment
- Installation of the Biren SDK and runtime components.
- Understanding the toolchain and compiler methodology.
- Basic project organization and build processes.
GPU Programming within the Biren Stack
- Thread and block execution models.
- Memory management and data transfer mechanisms.
- Kernel development and launch strategies.
Migrating 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
- Utilizing Biren’s debugger and profiling tools.
- Identifying system bottlenecks.
- Analyzing memory access patterns for optimization.
Advanced Optimization Techniques
- Thread scheduling and instruction pipelining.
- Loop unrolling and efficient shared memory utilization.
- Advanced kernel tuning for maximizing throughput.
Case Studies and Application Examples
- Training models using Biren accelerators.
- Porting and profiling vision or NLP models.
- Performance comparison against CUDA/NVIDIA solutions.
Summary and Future Directions
Requirements
- A solid grasp of GPU architecture and parallel processing concepts.
- Hands-on experience with CUDA, OpenCL, or comparable GPU programming environments.
- Working knowledge of deep learning frameworks such as PyTorch or TensorFlow.
Audience
- HPC developers.
- AI infrastructure engineers.
- Performance optimization specialists.
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.