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Course Outline

Introduction to Biren GPU Architecture

  • Overview of Biren technology and key use cases.
  • Detailed hardware layout, including cores, memory structures, and compute clusters.
  • Comparative analysis with NVIDIA and AMD GPU solutions.

Establishing the Biren Programming Environment

  • Installation of the Biren SDK and runtime components.
  • Exploring the toolchain and compiler architecture.
  • Understanding basic project structures and build workflows.

GPU Programming within the Biren Stack

  • Detailed look at thread and block execution models.
  • Strategies for memory management and efficient data transfer.
  • Techniques for kernel development and launch optimization.

Transitioning from CUDA to Biren

  • Methods for translating existing CUDA codebases.
  • Mapping common APIs and adapting code logic.
  • Interactive labs and practice sessions for code conversion.

Debugging and Profiling Strategies

  • Utilizing Biren’s integrated debugger and profiler tools.
  • Techniques for identifying performance bottlenecks.
  • Analyzing memory access patterns for optimization opportunities.

Advanced Optimization Techniques

  • Managing thread scheduling and instruction pipelining.
  • Applying loop unrolling and efficient shared memory usage.
  • Advanced kernel tuning strategies to maximize throughput.

Case Studies and Real-World Applications

  • Training machine learning models using Biren accelerators.
  • Practical exercises in porting and profiling vision or NLP models.
  • Performance benchmarking against CUDA/NVIDIA environments.

Course Summary and Future Steps

Requirements

  • Fundamental knowledge of GPU architecture and parallel processing concepts.
  • Hands-on experience with CUDA, OpenCL, or comparable GPU programming frameworks.
  • Proficiency with major deep learning frameworks such as PyTorch or TensorFlow.

Target Audience

  • Developers focused on high-performance computing.
  • Engineers specializing in AI infrastructure.
  • Specialists in performance optimization.
 21 Hours

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