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CUDA Kernel Optimization: Advanced GPU Performance Engineering

Master advanced CUDA kernel optimization techniques for high-performance GPU computing, covering memory patterns, warp efficiency, occupancy optimization, and cutting-edge performance profiling.

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πŸ”¬ Advanced Memory Optimization Patterns

In this section

Texture and Surface Memory#

Specialized Memory Types:#

🎨 Texture Memory Advantages:

  • Hardware Filtering: Automatic interpolation
  • Caching: Dedicated texture cache hierarchy
  • Bandwidth: Optimized for 2D spatial locality
  • Format Support: Native support for multiple data types

πŸ“‹ Memory Type Comparison:

Memory Type Bandwidth Latency Cache Best Use Case 
| Global      | 1.5 TB/s  | 400+ cycles | L2 only     | Large datasets    |
| Shared      | 19 TB/s   | 1-32 cycles | On-chip     | Block cooperation |
| Texture     | 1.2 TB/s  | 400+ cycles | Specialized | 2D/3D data        |
| Constant    | 1.5 TB/s  | 1-10 cycles | Dedicated   | Read-only data    |

Unified Memory and Stream Optimization#

Memory Management Architecture:#

🌊 Stream Processing Pipeline:

CPU Computation β†’ GPU Transfer β†’ Kernel Execution β†’ Result Transfer
     ↓              ↓               ↓                 ↓
  Overlapped    Asynchronous     Concurrent         Pipelined

⚑ Performance Optimization Strategies:

  • Memory Prefetching: Predictive data movement
  • Stream Parallelism: Concurrent kernel execution
  • Memory Pool Management: Reduced allocation overhead
  • Unified Memory Hints: Explicit data locality control
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