Advanced Learning Path

Master advanced GPU programming patterns including Ring Kernels, synchronization primitives, multi-GPU coordination, and performance profiling.

Prerequisites

• Completed Intermediate Path
• Strong understanding of GPU memory and kernel optimization
• Experience building multi-kernel pipelines

Learning Objectives

By completing this path, you will:

1. Build persistent GPU computations with Ring Kernels
2. Implement thread-safe synchronization patterns
3. Scale applications across multiple GPUs
4. Profile and optimize using GPU-native timing APIs

Modules

Module 1: Ring Kernel Fundamentals

Duration: 90-120 minutes

Learn persistent GPU computation with actor-style message passing.

Start Module 1 →

Module 2: Synchronization Patterns

Duration: 60-90 minutes

Master barriers, memory ordering, and multi-kernel coordination.

Start Module 2 →

Module 3: Multi-GPU Programming

Duration: 90-120 minutes

Scale applications across multiple GPUs with P2P transfers.

Start Module 3 →

Module 4: Performance Profiling

Duration: 60-90 minutes

Use GPU timing APIs for precise performance measurement and optimization.

Start Module 4 →

Completion Checklist

• [ ] Create a Ring Kernel with message processing
• [ ] Implement barrier-based synchronization
• [ ] Configure P2P memory transfers
• [ ] Profile kernel execution with GPU timestamps

Next Steps

After completing this path, continue to the Contributor Path to learn how to extend DotCompute or explore the comprehensive Ring Kernels Guide.

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Estimated total duration: 6-8 hours