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Duration 21 hours
Course Outline
Introduction to Biren GPU Architecture
- Overview of Biren and key use cases
- Hardware configuration: cores, memory, and compute clusters
- Comparative analysis with NVIDIA and AMD GPUs
Configuring the Biren Programming Environment
- Installing the Biren SDK and runtime components
- Exploring the toolchain and compiler model
- Understanding basic project structure and build workflows
GPU Programming with the Biren Stack
- Thread and block execution models
- Memory management and data transfer mechanisms
- Kernel development and launch patterns
Migrating from CUDA to Biren
- Strategies for translating CUDA code
- Common API mappings and necessary adaptations
- Hands-on code conversion labs
Debugging and Profiling
- Utilizing Biren’s debugger and profiler tools
- Identifying performance bottlenecks
- Optimizing memory access patterns
Optimization Techniques
- Thread scheduling and instruction pipelining
- Loop unrolling and effective use of shared memory
- Advanced kernel tuning for maximum throughput
Case Studies and Application Examples
- Training models using Biren accelerators
- Porting and profiling vision or NLP models
- Performance benchmarking against CUDA/NVIDIA
Summary and Next Steps
Requirements
- Solid understanding of GPU architecture and parallel processing principles
- Prior experience with GPU programming environments like CUDA or OpenCL
- Familiarity with deep learning frameworks such as PyTorch or TensorFlow
Target 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.
