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Duration 21 hours
Course Outline
Overview of Biren GPU Architecture
- Biren introduction and typical applications
- Hardware configuration: cores, memory, and compute clusters
- Benchmarking against NVIDIA and AMD GPUs
Configuring the Biren Programming Environment
- Installation of Biren SDK and runtime components
- Grasping the toolchain and compiler architecture
- Foundational project setup and build procedures
GPU Development via the Biren Stack
- Thread and block operational models
- Memory handling and data transfer mechanisms
- Kernel creation and execution patterns
Migrating Code from CUDA to Biren
- Strategies for translating CUDA codebases
- Mapping common APIs and necessary adaptations
- Practical sessions for code conversion and practice
Debugging and Performance Analysis
- Utilizing Biren’s integrated debugger and profiler
- Pinpointing performance bottlenecks
- Analyzing memory access patterns and optimization opportunities
Performance Optimization Strategies
- Thread scheduling and instruction pipelining techniques
- Loop unrolling and efficient shared memory utilization
- Advanced kernel adjustments for maximizing throughput
Real-World Case Studies and Applications
- Training machine learning models on Biren accelerators
- Porting and analyzing vision or NLP models
- Performance comparison against CUDA/NVIDIA standards
Course Wrap-up and Future Directions
Requirements
- A solid grasp of GPU architecture and parallel processing principles.
- Hands-on experience with CUDA, OpenCL, or comparable GPU programming frameworks.
- Proficiency with deep learning platforms such as PyTorch or TensorFlow.
Intended 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.