دوره جامع CUDA
CUDA یک پلتفرم محاسبات موازی و یک مدل برنامه‌نویسی است که توسط NVIDIA توسعه یافته است و توسعه‌دهندگان را قادر می‌سازد تا از قابلیت‌های پردازش موازی عظیم پردازنده‌های گرافیکی (GPU) برای کارهای محاسباتی همه منظوره، فراتر از رندر گرافیکی استفاده کنند. CUDA برای برنامه‌هایی که به قدرت محاسباتی بالایی نیاز دارند، مانند شبیه‌سازی‌های علمی، تجزیه و تحلیل داده‌ها، و یادگیری ماشین، که در آن زمان پردازش را به میزان قابل‌توجهی تسریع می‌کند، بسیار مهم است. یادگیری CUDA مهم است زیرا فرصت‌هایی را در زمینه‌هایی مانند هوش مصنوعی و یادگیری عمیق باز می‌کند، جایی که فریمورک‌هایی مانند TensorFlow و PyTorch برای شتاب GPU به CUDA متکی هستند.
یک معلم خصوصی می‌تواند با ارائه راهنمایی عملی در مورد راه اندازی محیط CUDA، درک سلسله مراتب thread، بهینه‌سازی استفاده از حافظه و اجرای توابع kernel، فرآیند یادگیری را تسهیل کند. مدرس خصوصی همچنین می‌تواند به یادگیرندگان کمک کند تا مفاهیم CUDA را در پروژه‌های دنیای واقعی به کار ببرند و از تجربه عملی و افزایش چشم‌انداز شغلی در صنایعی که به طور فزاینده‌ای به محاسبات با شتاب GPU متکی هستند، اطمینان حاصل کنند.
Chapter 1: Introduction to CUDA
- Lesson 1: What is CUDA? Overview and Importance
- Lesson 2: History and Evolution of CUDA
- Lesson 3: GPU vs. CPU Computing: Key Differences
- Lesson 4: Installing CUDA Toolkit on Windows, Linux, and macOS
- Lesson 5: Setting Up the Development Environment (NVIDIA Drivers, NVCC, IDEs)
Chapter 2: Understanding GPU Architecture
- Lesson 1: Basics of GPU Architecture
- Lesson 2: CUDA Compute Capability and SM Architecture
- Lesson 3: Thread Hierarchy: Blocks, Grids, and Warps
- Lesson 4: Memory Hierarchy: Shared, Global, Local, and Constant Memory
- Lesson 5: GPU Execution Model and Scheduling
Chapter 3: Writing Your First CUDA Program
- Lesson 1: Understanding the CUDA Programming Model
- Lesson 2: Structure of a CUDA Program
- Lesson 3: Kernel Functions and CUDA Thread Execution
- Lesson 4: Launching CUDA Kernels and Managing Threads
- Lesson 5: Debugging CUDA Programs
Chapter 4: Memory Management in CUDA
- Lesson 1: Memory Types in CUDA and Their Use Cases
- Lesson 2: Allocating and Freeing Device Memory (cudaMalloc, cudaFree)
- Lesson 3: Data Transfers Between Host and Device (cudaMemcpy)
- Lesson 4: Memory Optimization Techniques
- Lesson 5: Shared Memory and Bank Conflicts
Chapter 5: CUDA Threads and Synchronization
- Lesson 1: Thread Indexing and Grid Configuration
- Lesson 2: Using __syncthreads() for Synchronization
- Lesson 3: Thread Divergence and Warp Execution Efficiency
- Lesson 4: Atomic Operations and Reduction in CUDA
- Lesson 5: Performance Optimization Using Thread Synchronization
Chapter 6: CUDA Streams and Concurrency
- Lesson 1: Introduction to CUDA Streams
- Lesson 2: Overlapping Computation and Communication
- Lesson 3: Managing Multiple Streams
- Lesson 4: Asynchronous Memory Transfers
- Lesson 5: Stream Synchronization and Dependencies
Chapter 7: CUDA Memory Optimizations
- Lesson 1: Register Usage and Performance
- Lesson 2: Texture and Surface Memory
- Lesson 3: Efficient Memory Access Patterns
- Lesson 4: Using Pinned Memory for Faster Transfers
- Lesson 5: Memory Coalescing Strategies
Chapter 8: Parallel Algorithms in CUDA
- Lesson 1: Parallel Reduction
- Lesson 2: Prefix Sum (Scan) Algorithm
- Lesson 3: Parallel Histogram Computation
- Lesson 4: Matrix Multiplication Using CUDA
- Lesson 5: Sorting Algorithms in CUDA
Chapter 9: Using CUDA with OpenMP and MPI
- Lesson 1: Hybrid CPU-GPU Programming with OpenMP
- Lesson 2: Multi-GPU Programming with MPI and CUDA
- Lesson 3: CUDA-Aware MPI and Unified Memory
- Lesson 4: Load Balancing Between CPU and GPU
- Lesson 5: Case Study: Large-Scale Distributed CUDA Applications
Chapter 10: CUDA Unified Memory
- Lesson 1: Introduction to Unified Memory
- Lesson 2: Using cudaMallocManaged for Unified Memory Allocation
- Lesson 3: Understanding Page Migration and Prefetching
- Lesson 4: Performance Considerations in Unified Memory
- Lesson 5: Case Study: Unified Memory in Real-World Applications
Chapter 11: Debugging and Profiling CUDA Applications
- Lesson 1: Using CUDA Debugger (cuda-gdb)
- Lesson 2: Common CUDA Errors and Debugging Strategies
- Lesson 3: Using NVIDIA Nsight Tools for Profiling
- Lesson 4: Profiling Kernel Execution Time
- Lesson 5: Optimizing Memory Bandwidth and Kernel Performance
Chapter 12: CUDA Dynamic Parallelism
- Lesson 1: What is Dynamic Parallelism?
- Lesson 2: Launching Kernels from Kernels
- Lesson 3: Performance Impacts of Dynamic Parallelism
- Lesson 4: Use Cases of Dynamic Parallelism
- Lesson 5: Best Practices for Efficient Dynamic Parallelism
Chapter 13: CUDA Graphs
- Lesson 1: Introduction to CUDA Graphs
- Lesson 2: Creating and Executing CUDA Graphs
- Lesson 3: Recording CUDA Operations in a Graph
- Lesson 4: Performance Benefits of CUDA Graphs
- Lesson 5: Case Study: Optimizing Workflows with CUDA Graphs
Chapter 14: CUDA Tensor Cores and AI Applications
- Lesson 1: Introduction to Tensor Cores
- Lesson 2: Using Tensor Cores for Deep Learning
- Lesson 3: Accelerating Matrix Multiplication with Tensor Cores
- Lesson 4: Integrating CUDA with TensorFlow and PyTorch
- Lesson 5: Real-World AI and ML Applications with CUDA
Chapter 15: CUDA and Real-Time Graphics
- Lesson 1: CUDA and OpenGL Interoperability
- Lesson 2: CUDA and Vulkan Integration
- Lesson 3: GPU-Based Image Processing with CUDA
- Lesson 4: Real-Time Ray Tracing with CUDA
- Lesson 5: Case Study: CUDA in Game Development
Chapter 16: Multi-GPU Programming
- Lesson 1: Overview of Multi-GPU Programming
- Lesson 2: CUDA Peer-to-Peer Memory Access
- Lesson 3: Using Multi-GPU with CUDA Streams
- Lesson 4: Load Balancing Across Multiple GPUs
- Lesson 5: Case Study: Multi-GPU Scientific Computing
Chapter 17: CUDA for High-Performance Computing (HPC)
- Lesson 1: CUDA in Supercomputing
- Lesson 2: High-Performance Numerical Libraries (cuBLAS, cuFFT, cuSPARSE)
- Lesson 3: Using CUDA in Computational Fluid Dynamics (CFD)
- Lesson 4: CUDA in Genomic Data Processing
- Lesson 5: Case Study: CUDA in Weather Simulation
Chapter 18: Future Trends and New Features in CUDA
- Lesson 1: Latest Features in the Most Recent CUDA Releases
- Lesson 2: Advancements in GPU Hardware Architecture
- Lesson 3: Integrating CUDA with Quantum Computing
- Lesson 4: Emerging Trends in GPU Programming
- Lesson 5: Future of CUDA and Parallel Computing