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Duration 21 hours
Course Outline
Overview of Biren GPU Architecture
- Biren platform overview and key use cases
- Hardware configuration: cores, memory, and compute clusters
- Benchmarking against NVIDIA and AMD GPU architectures
Configuring the Biren Development Environment
- Installation of Biren SDK and runtime components
- Exploring the toolchain and compiler model
- Fundamental project structure and build workflows
Programming with the Biren GPU Stack
- Thread and block execution models
- Memory management strategies and data transfer mechanisms
- Kernel development and launch patterns
Migrating from CUDA to Biren
- Strategies for translating CUDA codebases
- Mapping common APIs and necessary adaptations
- Hands-on labs focused on code conversion
Debugging and Performance Profiling
- Leveraging Biren’s debugging and profiling tools
- Techniques for identifying performance bottlenecks
- Analysis of memory access patterns and optimization opportunities
Advanced Optimization Strategies
- Thread scheduling and instruction pipelining techniques
- Utilizing loop unrolling and shared memory efficiently
- Advanced kernel tuning to maximize throughput
Case Studies and Real-World Applications
- Model training workflows using Biren accelerators
- Porting and profiling vision or NLP models
- Performance comparison against CUDA/NVIDIA environments
Course Summary and Future Directions
Requirements
- A solid grasp of GPU architecture and parallel processing concepts.
- Prior experience working with CUDA, OpenCL, or comparable GPU programming frameworks.
- Familiarity with major deep learning frameworks, including PyTorch or TensorFlow.
Target Audience
- HPC developers.
- AI infrastructure engineers.
- Specialists in performance optimization.
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.