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 Duration 21 hours

Course Outline

Overview of Biren GPU Architecture

  • Biren platform overview and key use cases
  • Hardware configuration: cores, memory, and compute clusters
  • Benchmarking against NVIDIA and AMD GPU architectures

Configuring the Biren Development Environment

  • Installation of Biren SDK and runtime components
  • Exploring the toolchain and compiler model
  • Fundamental project structure and build workflows

Programming with the Biren GPU Stack

  • Thread and block execution models
  • Memory management strategies and data transfer mechanisms
  • Kernel development and launch patterns

Migrating from CUDA to Biren

  • Strategies for translating CUDA codebases
  • Mapping common APIs and necessary adaptations
  • Hands-on labs focused on code conversion

Debugging and Performance Profiling

  • Leveraging Biren’s debugging and profiling tools
  • Techniques for identifying performance bottlenecks
  • Analysis of memory access patterns and optimization opportunities

Advanced Optimization Strategies

  • Thread scheduling and instruction pipelining techniques
  • Utilizing loop unrolling and shared memory efficiently
  • Advanced kernel tuning to maximize throughput

Case Studies and Real-World Applications

  • Model training workflows using Biren accelerators
  • Porting and profiling vision or NLP models
  • Performance comparison against CUDA/NVIDIA environments

Course Summary and Future Directions

Requirements

  • A solid grasp of GPU architecture and parallel processing concepts.
  • Prior experience working with CUDA, OpenCL, or comparable GPU programming frameworks.
  • Familiarity with major deep learning frameworks, including PyTorch or TensorFlow.

Target Audience

  • HPC developers.
  • AI infrastructure engineers.
  • Specialists in performance optimization.

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