Advanced Cuda Programming

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• Advanced CUDA Programming: High Performance Computing with GPUs

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Chapter 1. Advanced CUDA Architecture Deep Dive

Section 1. Modern GPU Architecture

• Ampere/Hopper Architecture Details
• Streaming Multiprocessor Internals
• Memory Controller Design

Section 2. Advanced Thread Execution Model

• Warp Scheduling Mechanisms
• Branch Prediction and Divergence
• Instruction-Level Parallelism

Section 3. Memory System Internals

• Cache Hierarchy Implementation
• Memory Coalescing Mechanisms
• L2 Cache Optimization Strategies

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Chapter 2. Memory Management and Optimization

Section 1. Advanced Memory Patterns

• Custom Memory Allocators
• Memory Pool Implementation
• Zero-Copy Memory Strategies

Section 2. Unified Memory Programming

• Page Migration Engines
• Prefetch Optimization
• System-Wide Memory Access

Section 3. Memory Access Optimization

• Bank Conflict Resolution
• Shared Memory Access Patterns
• Cache Line Utilization

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Chapter 3. CUDA Streams and Asynchronous Programming

Section 1. Advanced Stream Management

• Multi-Stream Scheduling
• Stream Priority Control
• Event-Based Synchronization

Section 2. Asynchronous Memory Operations

• Overlapping Data Transfers
• Pinned Memory Usage
• Asynchronous Prefetching

Section 3. Advanced Synchronization

• Inter-Stream Dependencies
• CPU-GPU Synchronization
• Multi-GPU Coordination

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Chapter 4. Advanced Kernel Development

Section 1. Thread Block Optimization

• Dynamic Block Sizing
• Occupancy-Driven Design
• Resource Utilization

Section 2. Warp-Level Programming

• Warp Primitives
• Cooperative Groups
• Shuffle Instructions

Section 3. Dynamic Parallelism

• Recursive Kernel Launch
• Parent-Child Synchronization
• Resource Management

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Chapter 5. Performance Optimization Techniques

Section 1. Instruction-Level Optimization

• Assembly Analysis
• PTX Optimization
• Register Pressure Management

Section 2. Memory-Bound Optimization

• Memory Access Patterns
• Texture Memory Usage
• Constant Memory Optimization

Section 3. Compute-Bound Optimization

• Arithmetic Intensity
• Thread Coarsening
• Loop Unrolling Strategies

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Chapter 6. Advanced Data Structures for GPUs

Section 1. GPU-Optimized Containers

• Lock-Free Data Structures
• Concurrent Hash Tables
• Priority Queues

Section 2. Custom Memory Management

• Slab Allocators
• Memory Pools
• Defragmentation Techniques

Section 3. Sparse Data Structures

• Compressed Formats
• Dynamic Updates
• Efficient Traversal

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Chapter 7. Scientific Computing Applications

Section 1. Linear Algebra Implementations

• Custom BLAS Operations
• Sparse Matrix Operations
• Eigenvalue Solvers

Section 2. Numerical Methods

• FFT Implementation
• Differential Equations
• Monte Carlo Methods

Section 3. Optimization Algorithms

• Parallel Sort Implementation
• Graph Algorithms
• Numerical Optimization

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Chapter 8. Machine Learning and AI Acceleration

Section 1. Deep Learning Primitives

• Custom GEMM Implementation
• Convolution Optimization
• Tensor Core Programming

Section 2. Training Optimization

• Mixed Precision Training
• Memory-Efficient Training
• Multi-GPU Training

Section 3. Inference Optimization

• Quantization Techniques
• Kernel Fusion
• Batch Processing

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Chapter 9. Multi-GPU Programming

Section 1. Multi-GPU Communication

• P2P Communication
• NVLink Optimization
• Remote Memory Access

Section 2. Workload Distribution

• Load Balancing Strategies
• Memory Distribution
• Synchronization Methods

Section 3. Distributed Computing

• MPI Integration
• Multi-Node Systems
• Cluster Programming

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Chapter 10. Advanced Debugging and Profiling

Section 1. Performance Analysis

• Nsight Compute Usage
• Roofline Analysis
• Memory Access Patterns

Section 2. Advanced Debugging

• CUDA-GDB Techniques
• Memory Checking Tools
• Race Detection

Section 3. Optimization Tools

• Metrics Collection
• Visual Profiler
• Custom Profiling

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Chapter 11. Real-time Processing

Section 1. Low-Latency Techniques

• Kernel Scheduling
• Memory Management
• Pipeline Optimization

Section 2. Real-time Constraints

• Deterministic Execution
• Deadline Scheduling
• Resource Management

Section 3. Stream Processing

• Data Pipeline Design
• Continuous Processing
• Buffer Management

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Chapter 12. Advanced Topics and Future Directions

Section 1. Emerging Technologies

• Ray Tracing Cores
• New Memory Technologies
• Next-Gen Architecture

Section 2. Advanced Programming Models

• Graph Programming
• Quantum Simulation
• Domain-Specific Languages

Section 3. Industry Applications

• Case Studies
• Performance Analysis
• Best Practices

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