GPU Programming

6.

Advanced CUDA Programming

6.1.

Dynamic Parallelism

6.1.1.

Nested Kernel Launches

6.1.1.1.

Parent-Child Relationships

6.1.1.2.

Synchronization Semantics

6.1.1.3.

Memory Visibility

6.1.2.

Use Cases and Applications

6.1.2.1.

Adaptive Algorithms

6.1.2.2.

6.1.2.3.

Recursive Problems

6.1.3.

Performance Considerations

6.1.3.1.

Launch Overhead

6.1.3.2.

Memory Management

6.1.3.3.

Debugging Challenges

6.2.

Multi-GPU Programming

6.2.1.

Multi-GPU Architectures

6.2.1.1.

Peer-to-Peer Access

6.2.1.2.

NVLink Technology

6.2.1.3.

Memory Topology

6.2.2.

Programming Patterns

6.2.2.1.

Data Parallelism

6.2.2.2.

Model Parallelism

6.2.2.3.

Pipeline Parallelism

6.2.3.

Communication Strategies

6.2.3.1.

Direct Memory Access

6.2.3.2.

6.2.3.3.

6.3.

CUDA Libraries Ecosystem

6.3.1.

Mathematical Libraries

6.3.1.1.

6.3.1.2.

6.3.1.3.

6.3.1.4.

6.3.2.

Machine Learning Libraries

6.3.2.1.

6.3.2.2.

6.3.2.3.

6.3.3.

Utility Libraries

6.3.3.1.

6.3.3.2.

6.3.3.3.

6.3.3.4.

6.4.

Interoperability

6.4.1.

Graphics API Integration

6.4.1.1.

6.4.1.2.

DirectX Interop

6.4.1.3.

6.4.2.

CPU Library Integration

6.4.2.1.

MPI Integration

6.4.2.2.

OpenMP Integration

6.4.2.3.

Threading Libraries

6.5.

Specialized Hardware Features

6.5.1.

6.5.1.1.

Architecture Overview

6.5.1.2.

Mixed-Precision Computing

6.5.1.3.

Programming Models

6.5.1.4.

Performance Optimization

6.5.2.

6.5.2.1.

Ray Tracing Acceleration

6.5.2.2.

OptiX Integration

6.5.2.3.

Hybrid Rendering

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