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 Duration 21 hours

Course Outline

Foundations of Biren GPU Architecture

  • Overview of Biren technology and key application scenarios.
  • Hardware configuration: examining cores, memory structures, and compute clusters.
  • Comparative analysis with NVIDIA and AMD GPU architectures.

Configuring the Biren Development Ecosystem

  • Installation of the Biren SDK and runtime components.
  • Understanding the toolchain mechanics and compiler model.
  • Reviewing basic project structures and build workflows.

Programming with the Biren Stack

  • Exploring thread and block organizational models.
  • Managing memory resources and data transfer operations.
  • Developing kernels and understanding launch patterns.

Migrating from CUDA to Biren

  • Strategies for translating CUDA codebases.
  • Mapping common APIs and handling necessary adaptations.
  • Hands-on labs and practice sessions for code conversion.

Diagnostic Tools: Debugging and Profiling

  • Leveraging Biren’s integrated debugger and profiler.
  • Techniques for identifying performance bottlenecks.
  • Optimizing memory access patterns and execution flow.

Advanced Optimization Strategies

  • Managing thread scheduling and instruction pipelining.
  • Utilizing loop unrolling and shared memory effectively.
  • Refining kernel performance for maximum throughput.

Practical Applications and Case Studies

  • Training machine learning models utilizing Biren accelerators.
  • Porting and profiling vision or NLP models.
  • Performance benchmarking against CUDA/NVIDIA solutions.

Recap and Future Directions

Requirements

  • Solid knowledge of GPU architecture and parallel processing concepts.
  • Prior experience working with CUDA, OpenCL, or analogous GPU programming frameworks.
  • Proficiency with major deep learning frameworks, such as PyTorch or TensorFlow.

Target Audience

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

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