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Duration 21 hours
Course Outline
Overview of Biren GPU Architecture
- Introduction to Biren and its primary use cases
- Hardware structure: examining cores, memory, and compute clusters
- Benchmarking against NVIDIA and AMD GPU capabilities
Preparing the Biren Development Environment
- Installing the Biren SDK and associated runtime components
- Grasping the toolchain and compiler logic
- Exploring basic project structures and build workflows
Programming with the Biren Software Stack
- Managing thread and block models
- Handling memory management and data transfer operations
- Developing kernels and defining launch patterns
Migrating Code from CUDA to Biren
- Strategies for translating CUDA codebases
- Mapping common APIs and necessary adaptations
- Practical labs focused on code conversion
Debugging and Performance Profiling
- Leveraging Biren’s integrated debugger and profiler
- Pinpointing performance bottlenecks
- Analyzing memory access patterns for optimization
Advanced Optimization Strategies
- Refining thread scheduling and instruction pipelining
- Utilizing loop unrolling and shared memory effectively
- Fine-tuning kernels for maximum throughput
Practical Case Studies and Applications
- Training machine learning models using Biren accelerators
- Porting and profiling vision or NLP model workloads
- Evaluating performance in comparison to CUDA/NVIDIA ecosystems
Recap and Future Directions
Requirements
- A solid grasp of GPU architecture and parallel processing concepts
- Hands-on experience with CUDA, OpenCL, or comparable GPU programming frameworks
- Proficiency with deep learning frameworks like PyTorch or TensorFlow
Target Audience
- High-Performance Computing (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.