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Course Outline
Introduction to Biren GPU Architecture
- Overview of Biren and its primary use cases
- Hardware components: cores, memory structures, and compute clusters
- Comparative analysis with NVIDIA and AMD GPU architectures
Establishing the Biren Programming Environment
- Installation of the Biren SDK and runtime components
- Insights into the toolchain and compiler model
- Understanding basic project structures and build workflows
GPU Programming via the Biren Stack
- Exploring thread and block models
- Managing memory and handling data transfers
- Developing kernels and establishing launch patterns
Migrating from CUDA to Biren
- Techniques for translating CUDA codebases
- Mapping common APIs and making necessary adaptations
- Practical labs and exercises for code conversion
Debugging and Profiling Strategies
- Utilizing Biren’s debugger and profiling tools
- Detecting performance bottlenecks
- Optimizing memory access patterns
Advanced Optimization Techniques
- Thread scheduling and instruction pipelining
- Loop unrolling and efficient use of shared memory
- Fine-tuning kernels for maximum throughput
Case Studies and Application Examples
- Training models using Biren accelerators
- Porting and profiling vision or NLP models
- Benchmarking performance against CUDA/NVIDIA solutions
Summary and Future Steps
Requirements
- A solid understanding of GPU architecture and parallel processing concepts
- Practical experience with CUDA, OpenCL, or comparable GPU programming frameworks
- Proficiency with deep learning frameworks like PyTorch or TensorFlow
Target Audience
- HPC developers
- AI infrastructure engineers
- Performance optimization specialists
21 Hours
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.