Chapter 12. Warp-Level Primitives
Section 1. Warp Shuffle Instructions
- Variants (shfl.sync, shfl.up, shfl.down, shfl.bfly)
- Data sharing patterns (reductions, prefix sum, broadcast, transpose)
- Shuffle performance (latency, divergence considerations)
Section 2. Vote Instructions
- Variants (vote.all, vote.any, vote.uni, vote.ballot)
- Applications (early termination, predicate computation, dynamic scheduling)
Section 3. Warp-Level Synchronization
- Implicit sync in SIMT
- Explicit bar.warp.sync
- Independent thread scheduling implications
Section 4. Warp Specialization Techniques
- Producer-consumer patterns
- Persistent kernels
- Warp specialization for tasks
- Load balancing
Section 5. Match and Reduce
- match.any.sync and duplicates
- Warp-level hash table operations
Chapter 13. Atomic Operations and Synchronization
Section 1. Atomic Memory Operations
- Basic atomics (add, sub, min, max, inc, dec, exch, cas)
- Supported state spaces (.global, .shared)
- Scope modifiers (.gpu, .sys, .cluster)
- Semantic modes (.relaxed, .acquire, .release, .acq_rel)
Section 2. Atomic Performance
- Throughput and serialization effects
- Optimization strategies
Section 3. Reduction Operations
- Global reduction atomics vs atom
- Use cases
Section 4. Compare-and-Swap Patterns
- Lock-free data structures
- Optimistic concurrency
- ABA problem and mitigations
Section 5. Cluster Synchronization
- Cluster barriers
- Cluster reductions
- Distributed shared memory atomics
Section 6. Memory Ordering and Consistency
- Happens-before relationships
- Acquire-release semantics
- Memory model examples
Section 7. Advanced Synchronization Patterns
- Producer-consumer queues
- Reader-writer locks
- Counting barriers
- Phased synchronization
Section 8. Scope and Performance Tradeoffs
- Thread, warp, block, cluster level tradeoffs
- Optimization decision tree
Chapter 14. Special Instructions and System Operations
Section 1. Video and Image Processing Instructions
- Video decode and encode hints
- Pixel format conversions
- Sub-byte data manipulation
Section 2. Graphics Interop Instructions
- Surface memory operations
- Texture sampling in compute
- Ray tracing hint instructions
Section 3. Cryptography and Security
- AES instructions
- Hash acceleration
- RNG support
Section 4. System-Level Operations
- Device management
- Debugging support
- ABI and calling conventions
Section 5. Inline Assembly Integration
- Mixing PTX with CUDA C++
- Constraint specifications
- Custom intrinsic implementation example
Section 6. Runtime Compilation (NVRTC)
- Dynamic PTX generation
- Just-in-time optimization
- Domain-specific languages and adaptive kernels
Section 7. Multi-GPU and NVLink Operations
- Peer access
- Inter-GPU transfers
- NVLink atomics
Chapter 15. Memory Optimization Masterclass
Section 1. Memory Hierarchy Recap and Strategy
- Latency hiding through occupancy
- Bandwidth optimization
- Roofline model applied to PTX
Section 2. Global Memory Optimization
- Coalescing mastery
- Cache behavior and PTX hints
- Prefetching and async copy
Section 3. Shared Memory Optimization
- Bank conflict elimination
- Shared memory capacity and occupancy effects
Section 4. Register Optimization
- Pressure management
- Reuse strategies and manual management
Section 5. Texture and Constant Memory
- Optimal use cases
- Broadcast patterns and read-only optimizations
Section 6. Memory Access Analysis Tools
- Nsight Compute memory analysis
- Manual PTX and SASS inspection
- Benchmarking methodology
Section 7. Case Studies
- Matrix transpose optimization
- Reduction algorithm comparison
- Stencil computation optimization
Chapter 16. Compute Optimization and Instruction-Level Tuning
Section 1. Instruction Throughput
- Latency and throughput by architecture
- SP vs DP performance
- SFU utilization
- Integer vs floating-point tradeoffs
Section 2. Instruction-Level Parallelism
- Dependency analysis
- Loop unrolling and pipelining
Section 3. Dual-Issue and Instruction Pairing
- Architecture-specific dual-issue rules
- Instruction mixing for throughput
Section 4. Divergence Minimization
- Understanding divergence costs
- Reduction strategies
Section 5. Occupancy Optimization
- Theory of active warps
- Occupancy-performance relationship
Section 6. Mixed-Precision Optimization
- FP16, BF16, TF32 strategies
- Precision vs performance tradeoffs
Section 7. Compiler Optimization Flags
- ptxas options
- CUDA compilation flags
- Architecture-specific tuning
Section 8. SASS-Level Tuning
- Scheduler decisions
- Manual interventions
- Knowing when to stop
Chapter 17. Advanced Optimization Techniques
Section 1. Kernel Fusion
- Eliminating intermediate memory traffic
- Limitations and tradeoffs
Section 2. Persistent Kernels
- Launch overhead reduction
- Work stealing and load balancing
- Termination strategies
Section 3. Cooperative Groups
- Thread block clusters
- Dynamic warp formation
- Synchronization patterns
Section 4. Multi-GPU Optimization
- Work distribution strategies
- NVLink utilization
- Peer-to-peer transfers
Section 5. Dynamic Parallelism
- Child kernel launches
- Use cases and limitations
Section 6. Streams and Concurrency
- Overlap of compute and memory
- Multi-stream patterns
Section 7. Power and Thermal Optimization
- DVFS awareness
- Instruction mix for power efficiency
Section 8. Algorithmic Optimization
- GPU-friendly algorithms
- Data structure design
- Tiling for problem sizes
Section 9. AI-Driven Optimization
- Auto-tuning frameworks
- Reinforcement learning for scheduling
- Evolutionary search methods
Chapter 18. Debugging and Profiling
Section 1. Debugging PTX Code
- cuda-gdb debugging workflow
- Nsight debugger visual inspection
- Compute Sanitizer for race and memory detection
Section 2. Profiling with Nsight Compute
- Performance metrics
- PTX and SASS analysis
- Guided bottleneck analysis
Section 3. Nsight Systems
- Timeline visualization
- Kernel duration analysis
- Multi-GPU profiling
Section 4. Manual Instrumentation
- Cycle counters (%clock, %clock64)
- Custom timers and event logging
Chapter 19. Tools, Ecosystem, and Future Directions
Section 1. Companion Resources
- GitHub repository and Docker containers
- Online community and forums
- Updates for new CUDA and PTX versions
Section 2. Ecosystem Integration
- CUTLASS and cuBLAS comparisons
- Machine learning framework integration
- Compiler toolchains and research DSLs
Section 3. Research and Emerging Directions
- AI-assisted kernel optimization
- Future ISA features
- Hardware roadmap
Section 4. Practical Outlook
- Industry applications in AI, HPC, and scientific computing
- Performance engineering as a discipline
- Advice for practitioners