Chapter 1. GPU Assembly Fundamentals

Section 1. GPU ISA Architecture Deep Dive

• Binary encoding and instruction formats
• Microarchitectural pipeline stages
• Vector and scalar execution units
• Hardware thread scheduling mechanisms
• Clock domains and synchronization barriers

Section 2. Memory System Architecture

• Memory controller design and protocols
• Cache line states and coherency protocols
• Memory fence operations and atomics
• Page table structures and TLB organization
• Memory compression algorithms

Section 3. Execution Model Implementation

• Warp/wavefront scheduling algorithms
• Instruction issue and dispatch logic
• Branch prediction and speculation
• Predication and mask operations
• Hardware synchronization primitives

Chapter 2. Assembly Language Specifics

Section 1. Instruction Set Deep Dive

• Opcode formats and encoding schemes
• Immediate value handling
• Predicate registers and condition codes
• Special function unit instructions
• Vector mask operations

Section 2. Register Architecture

• Register file organization
• Register bank conflicts
• Register allocation algorithms
• Spill/fill optimization techniques
• Vector register partitioning

Section 3. Memory Access Patterns

• Cache line alignment requirements
• Stride pattern optimization
• Bank conflict avoidance
• Scatter/gather operation implementation
• Atomic operation mechanics

Chapter 3. AMD GPU Assembly Architecture

Section 1. GCN/RDNA ISA Technical Details

• Instruction word encoding formats
• Scalar and vector ALU implementations
• Local Data Share architecture
• Wave32/Wave64 execution models
• Hardware scheduler implementation

Section 2. AMD Memory System

• L0/L1/L2 cache architectures
• Memory controller interface specs
• Cache coherency protocols
• Page table walker implementation
• Memory view hierarchy

Section 3. AMD Performance Optimization

• VGPR/SGPR allocation strategies
• Instruction bundling techniques
• Cache bypass mechanisms
• Memory barrier optimization
• Wave item permutation techniques

Chapter 4. NVIDIA GPU Assembly Architecture

Section 1. PTX/SASS Technical Implementation

• PTX instruction encoding
• SASS optimization patterns
• Predication implementation
• Branch synchronization mechanics
• Warp shuffle operation details

Section 2. NVIDIA Memory Architecture

• Shared memory bank organization
• L1/TEX cache implementation
• Global memory coalescing rules
• Memory consistency model
• Atomic operation implementation

Section 3. NVIDIA Performance Engineering

• Register dependency chains
• Instruction latency hiding
• Memory transaction coalescing
• Warp scheduling optimization
• Tensor core matrix operation details

Chapter 5. Cross-Vendor Techniques

Section 1. Comparative Analysis

• Key architectural differences between AMD and NVIDIA GPUs
• ISA-level comparisons
• Execution model trade-offs

Section 2. Portable Assembly Code

• OpenCL, Vulkan, and SPIR-V
• Adapting AMD optimizations for NVIDIA GPUs (and vice versa)
• Strategies for platform-specific gains

Section 3. Cross-Vendor Debugging and Profiling

• Using RenderDoc and GDB for cross-platform analysis
• Bottleneck identification and resolution
• Ensuring performance parity across GPUs

Chapter 6. Low-Level Optimization Strategies

Section 1. Memory System Optimization

• Cache line state manipulation
• TLB optimization techniques
• Memory controller queue management
• Memory barrier minimization
• Atomic operation alternatives

Section 2. Instruction Scheduling

• Dependency chain analysis
• Resource conflict avoidance
• Instruction reordering techniques
• Loop unrolling strategies
• Software pipelining methods

Section 3. Register Optimization

• Register pressure analysis
• Live range splitting
• Register coalescing techniques
• Spill code optimization
• Register renaming strategies

Chapter 7. Practical Applications

Section 1. Scientific Computing

• FFT optimization techniques
• Stencil computation methods
• Sparse matrix optimization
• Random number generation

Section 2. Real-Time Graphics

• Ray tracing at the assembly level
• Optimizing Vulkan shaders
• Texture sampling techniques

Section 3. Machine Learning

• Convolution implementation
• Batch normalization techniques
• Gradient computation optimization

Chapter 8. Performance Analysis Techniques

Section 1. Performance Counters

• Hardware counter interpretation
• Event sampling methods
• Pipeline stall analysis
• Cache miss classification
• Memory bandwidth analysis

Section 2. Optimization Methodology

• Static code analysis
• Dynamic execution tracing
• Bottleneck identification
• Resource utilization analysis
• Latency/throughput optimization

Chapter 9. Emerging Trends in GPU Assembly

Section 1. Next-Generation Architectures

• Upcoming trends in GPU ISA design (RDNA3, Hopper)
• Unified memory and ray tracing implications
• Specialized hardware accelerators (tensor cores, AI chips)

Section 2. Future of Low-Level Programming

• AI-driven code generation and profiling
• Opportunities for low-level developers
• Evolution of tools and techniques

Chapter 10. Advanced Development Tools

Section 1. Assembly Development Tools

• Binary analysis techniques
• Disassembly methods
• Code generation tools
• Performance modeling
• Debugging techniques

Section 2. Profiling Implementation

• Sampling methods
• Trace collection and visualization
• Bottleneck analysis and optimization validation