Schulungsübersicht

Introduction to Biren GPU Architecture

  • Biren overview and use cases
  • Hardware layout: cores, memory, compute clusters
  • Comparison with NVIDIA and AMD GPUs

Setting Up the Biren Programming Environment

  • Installing Biren SDK and runtime
  • Understanding the toolchain and compiler model
  • Basic project structure and build process

GPU Programming with the Biren Stack

  • Thread and block models
  • Memory management and data transfers
  • Kernel development and launch patterns

Porting from CUDA to Biren

  • Translation techniques for CUDA code
  • Common API mappings and adaptations
  • Code conversion labs and practice

Debugging and Profiling

  • Using Biren’s debugger and profiler
  • Identifying bottlenecks
  • Memory access patterns and optimization

Optimization Techniques

  • Thread scheduling and instruction pipelining
  • Loop unrolling and shared memory use
  • Advanced kernel tuning for throughput

Case Study and Application Examples

  • Training a model with Biren accelerators
  • Porting and profiling a vision or NLP model
  • Comparing performance vs CUDA/NVIDIA

Summary and Next Steps

Voraussetzungen

  • Eine Grundkenntnis der GPU Architektur und Parallelausführung
  • Erfahrung mit CUDA, OpenCL oder vergleichbaren GPU-Programmierumgebungen
  • Bekanntschaft mit Deep-Learning-Frameworks wie PyTorch oder TensorFlow

Zielgruppe

  • HPC-Entwickler
  • AI-Infrastukturengineer
  • Fachkräfte für die Leistungsveredelung
 21 Stunden

Teilnehmerzahl


Price per participant (excl. VAT)

Erfahrungsberichte (1)

Kommende Kurse

Verwandte Kategorien