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Course Outline
Introduction to Biren GPU Architecture
- Overview of Biren and its primary use cases.
- Hardware configuration: cores, memory, and compute clusters.
- Comparative analysis with NVIDIA and AMD GPUs.
Configuring the Biren Programming Environment
- Installation of the Biren SDK and runtime.
- Insight into the toolchain and compiler model.
- Basic project structure and build workflows.
GPU Programming within the Biren Stack
- Thread and block models.
- Memory management and data transfer mechanisms.
- Kernel development and launch strategies.
Migration from CUDA to Biren
- Techniques for translating CUDA code.
- Common API mappings and necessary adaptations.
- Hands-on code conversion labs and practice sessions.
Debugging and Profiling
- Utilizing Biren’s debugger and profiler tools.
- Detecting performance bottlenecks.
- Analyzing memory access patterns for optimization.
Optimization Strategies
- Thread scheduling and instruction pipelining.
- Loop unrolling and leveraging shared memory.
- Advanced kernel tuning for enhanced throughput.
Case Studies and Practical Applications
- Training a model using Biren accelerators.
- Porting and profiling a vision or NLP model.
- Performance comparison against CUDA/NVIDIA solutions.
Conclusion and Next Steps
Requirements
- Proficiency in GPU architecture and parallel processing concepts.
- Practical experience with CUDA, OpenCL, or comparable GPU programming environments.
- Familiarity with deep learning frameworks such as 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.