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Duration 21 hours
Course Outline
Introduction to Biren GPU Architecture
- Overview of Biren and its primary use cases.
- Detailed look at hardware layout, including cores, memory, and compute clusters.
- Comparative analysis with NVIDIA and AMD GPU architectures.
Configuring the Biren Programming Environment
- Installation of the Biren SDK and runtime components.
- Understanding the toolchain and compiler models.
- Overview of basic project structures and build workflows.
GPU Programming Using the Biren Stack
- Exploring thread and block models.
- Managing memory and handling data transfers.
- Developing kernels and defining launch patterns.
Transitioning from CUDA to Biren
- Techniques for translating CUDA code.
- Mapping common APIs and making necessary adaptations.
- Practical labs focused on code conversion.
Debugging and Performance Profiling
- Utilizing Biren’s debugger and profiler tools.
- Identifying system bottlenecks.
- Optimizing memory access patterns.
Advanced Optimization Techniques
- Thread scheduling and instruction pipelining.
- Leveraging loop unrolling and shared memory.
- Advanced kernel tuning to maximize throughput.
Case Studies and Real-World Applications
- Training models using Biren accelerators.
- Porting and profiling vision or NLP models.
- Benchmarking performance against CUDA/NVIDIA solutions.
Conclusion and Future Directions
Requirements
- A solid understanding of GPU architecture and parallel processing concepts.
- Hands-on experience with CUDA, OpenCL, or equivalent GPU programming frameworks.
- Knowledge of deep learning frameworks such as 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.
