CUDA Programming Guide
Comprehensive guide for CUDA kernel development and optimization with DotCompute.
🚧 Documentation In Progress - CUDA programming guide is being developed.
Overview
DotCompute's CUDA backend supports:
- NVIDIA GPUs with Compute Capability 5.0-8.9
- CUDA 12.0+ toolkit
- Both CUBIN and PTX compilation paths
- NVRTC just-in-time compilation
CUDA Kernel Basics
Kernel Syntax
TODO: Document CUDA C kernel syntax
Thread Hierarchy
TODO: Explain thread blocks and grids
Memory Model
TODO: Document CUDA memory model:
- Global memory
- Shared memory
- Local registers
- Constant memory
Compute Capability
Detection and Targeting
TODO: Cover compute capability detection:
- Architecture-specific optimization
- Feature availability
Capability Levels
TODO: Document different compute capabilities:
- CC 5.0 (Maxwell) features
- CC 6.0-6.2 (Pascal) features
- CC 7.0-7.5 (Volta/Turing) features
- CC 8.0+ (Ampere/Ada) features
Optimization Techniques
Memory Coalescing
TODO: Explain global memory coalescing patterns
Shared Memory Usage
TODO: Document shared memory optimization:
- Bank conflicts
- Memory access patterns
- Synchronization
Register Pressure
TODO: Cover register usage optimization
Occupancy
TODO: Explain occupancy calculations and optimization
Synchronization
Warp-Level Operations
TODO: Document warp-level primitives:
- Shuffle operations
- Cooperative groups
Block-Level Synchronization
TODO: Explain block-level barriers
Global Synchronization
TODO: Cover global synchronization patterns
Advanced Features
Cooperative Groups
TODO: Document cooperative groups usage
Dynamic Parallelism
TODO: Explain dynamic kernel launches
Atomic Operations
TODO: Cover atomic memory operations
Debugging
CUDA Debugging Tools
TODO: List debugging tools:
- cuda-gdb
- Nsight Visual Studio Code Extension
- Compute Sanitizer
Performance Profiling
Nsight Tools
TODO: Document profiling tools:
- Nsight Systems
- Nsight Compute
Examples
TODO: Provide CUDA kernel examples