Updated the content of each modules

Author: coketaste

HIP_Course.md

@@ -2,9 +2,9 @@
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![CUDA](https://img.shields.io/badge/CUDA-12.9.1-76B900?logo=nvidia)](https://developer.nvidia.com/cuda-toolkit)
-[![ROCm](https://img.shields.io/badge/ROCm-latest-red?logo=amd)](https://rocmdocs.amd.com/)
+[![ROCm](https://img.shields.io/badge/ROCm-7.0-red?logo=amd)](https://rocmdocs.amd.com/)
 [![Docker](https://img.shields.io/badge/Docker-Ready-2496ED?logo=docker)](https://www.docker.com/)
-[![Examples](https://img.shields.io/badge/Examples-70%2B-green)](modules/)
+[![Examples](https://img.shields.io/badge/Examples-71-green)](modules/)
 [![CI](https://img.shields.io/badge/CI-GitHub%20Actions-2088FF?logo=github-actions)](https://github.com/features/actions)
 
 **A comprehensive, hands-on educational project for mastering GPU programming with CUDA and HIP**
@@ -35,7 +35,7 @@
 **GPU Programming 101** is a complete educational resource for learning modern GPU programming. This project provides:
 
 - **9 comprehensive modules** covering beginner to expert topics
-- **70+ working code examples** in both CUDA and HIP
+- **71 working code examples** in both CUDA and HIP
 - **Cross-platform support** for NVIDIA and AMD GPUs  
 - **Production-ready development environment** with Docker
 - **Professional tooling** including profilers, debuggers, and CI/CD
@@ -197,10 +197,11 @@ This architectural knowledge is essential for writing efficient GPU code and is
 |---------|-------------|
 | 🎯 **Complete Curriculum** | 9 progressive modules from basics to advanced topics |
 | 💻 **Cross-Platform** | Full CUDA and HIP support for NVIDIA and AMD GPUs |
-| 🐳 **Docker Ready** | Complete containerized development environment |
-| 🔧 **Production Quality** | Professional build systems, testing, and profiling |
+| 🐳 **Docker Ready** | Complete containerized development environment with CUDA 12.9.1 & ROCm 7.0 |
+| 🔧 **Production Quality** | Professional build systems, auto-detection, testing, and profiling |
 | 📊 **Performance Focus** | Optimization techniques and benchmarking throughout |
 | 🌐 **Community Driven** | Open source with comprehensive contribution guidelines |
+| 🧪 **Advanced Libraries** | Support for Thrust, MIOpen, and production ML frameworks |
 
 ## 🚀 Quick Start
 
@@ -224,7 +225,7 @@ cd modules/module1 && make && ./build/01_vector_addition_cuda
 For direct system installation:
 
 ```bash
-# Prerequisites: CUDA 11.0+ or ROCm 5.0+, GCC 7+, Make
+# Prerequisites: CUDA 12.0+ or ROCm 7.0+, GCC 9+, Make
 
 # Clone and build
 git clone https://github.com/AIComputing101/gpu-programming-101.git
@@ -265,7 +266,7 @@ Our comprehensive curriculum progresses from fundamental concepts to production-
 | [**Module 8**](modules/module8/) | 🚀 Expert | 10-12h | **Domain Applications** | ML, Scientific Computing | 4 |
 | [**Module 9**](modules/module9/) | 🚀 Expert | 6-8h | **Production Deployment** | Libraries, Integration, Scaling | 4 |
 
-**📈 Progressive Learning Path: 70+ Examples • 50+ Hours • Beginner to Expert**
+**📈 Progressive Learning Path: 71 Examples • 50+ Hours • Beginner to Expert**
 
 ### Learning Progression
 
@@ -313,7 +314,7 @@ Module 5: Performance Tuning
 ### Software Requirements
 
 #### Operating System Support
-- **Linux** (Recommended): Ubuntu 22.04 LTS, RHEL 8/9, SLES 15 SP5
+- **Linux** (Recommended): Ubuntu 22.04/24.04 LTS, RHEL 8/9, SLES 15 SP5
 - **Windows**: Windows 10/11 with WSL2 recommended for optimal compatibility
 - **macOS**: macOS 12+ (Metal Performance Shaders for basic GPU compute)
 
@@ -322,7 +323,7 @@ Module 5: Performance Tuning
   - **Driver Requirements**: 
     - Linux: 550.54.14+ for CUDA 12.4+
     - Windows: 551.61+ for CUDA 12.4+
-- **ROCm Platform**: 6.0+ (Docker uses ROCm latest)
+- **ROCm Platform**: 7.0+ (Docker uses ROCm 7.0)
   - **Driver Requirements**: Latest AMDGPU-PRO or open-source AMDGPU drivers
   - **Kernel Support**: Linux kernel 5.4+ recommended
 
@@ -338,6 +339,8 @@ Module 5: Performance Tuning
 - **Profiling**: Nsight Compute, Nsight Systems (NVIDIA), rocprof (AMD)
 - **Debugging**: cuda-gdb, rocgdb, compute-sanitizer
 - **Libraries**: cuBLAS, cuFFT, rocBLAS, rocFFT (for advanced modules)
+- **ML Libraries**: Thrust (NVIDIA), MIOpen (AMD) for deep learning applications
+- **System Management**: NVML (NVIDIA), ROCm SMI (AMD) for hardware monitoring
 
 ### Performance Expectations by Hardware Tier
 
@@ -381,28 +384,42 @@ Experience the full development environment with zero setup:
 - 📦 Isolated and reproducible builds
 - 🧹 Easy cleanup when done
 
+**Container Specifications:**
+- **CUDA**: NVIDIA CUDA 12.9.1 on Ubuntu 22.04
+- **ROCm**: AMD ROCm 7.0 on Ubuntu 24.04 
+- **Libraries**: Production-ready toolchains with debugging support
+
 **[📖 Complete Docker Guide →](docker/README.md)**
 
 ## 🔧 Build System
 
+Our advanced build system features automatic GPU vendor detection and optimized configurations:
+
 ### Project-Wide Commands
 ```bash
-make all           # Build all modules
+make all           # Build all modules with auto-detection
 make test          # Run comprehensive tests  
 make clean         # Clean all artifacts
-make check-system  # Verify GPU setup
+make check-system  # Verify GPU setup and dependencies
 make status        # Show module completion status
 ```
 
 ### Module-Specific Commands
 ```bash
 cd modules/module1/examples
-make               # Build all examples in module
+make               # Build all examples with vendor auto-detection
 make test          # Run module tests
 make profile       # Performance profiling
 make debug         # Debug builds with extra checks
 ```
 
+### Advanced Build Features
+- **Automatic GPU Detection**: Detects NVIDIA/AMD hardware and builds accordingly
+- **Production Optimization**: `-O3`, fast math, architecture-specific optimizations
+- **Debug Support**: Full debugging symbols and validation checks
+- **Library Management**: Automatic detection of optional dependencies (NVML, MIOpen)
+- **Cross-Platform**: Single Makefile supports both CUDA and HIP builds
+
 ##  Performance Expectations
 
 | Module Level | Typical GPU Speedup | Memory Efficiency | Code Quality |

README.md

@@ -1,7 +1,7 @@
 # Module 1: Foundations of GPU Programming with CUDA and HIP
 *Heterogeneous Data Parallel Computing*
 
-> Environment note: Examples are validated in containers using CUDA 12.9.1 (Ubuntu 22.04) and ROCm latest (rocm/dev-ubuntu-22.04:latest). Using Docker is recommended for a consistent setup.
+> Environment note: Examples are validated in containers using CUDA 12.9.1 (Ubuntu 22.04) and ROCm 7.0 (Ubuntu 24.04). The advanced build system automatically detects your GPU vendor and optimizes accordingly. Using Docker is recommended for a consistent setup.
 
 ## Learning Objectives
 After completing this module, you will be able to:
@@ -127,7 +127,7 @@ nvidia-smi
 wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
 sudo dpkg -i cuda-keyring_1.1-1_all.deb
 sudo apt-get update
-sudo apt-get -y install cuda-toolkit-12-4
+sudo apt-get -y install cuda-toolkit-12-6
 
 # Add to PATH
 echo 'export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}' >> ~/.bashrc
@@ -145,9 +145,9 @@ nvidia-smi
 
 **Step 1: Install ROCm**
 ```bash
-# Ubuntu 22.04
-wget https://repo.radeon.com/amdgpu-install/6.0/ubuntu/jammy/amdgpu-install_6.0.60000-1_all.deb
-sudo apt install ./amdgpu-install_6.0.60000-1_all.deb
+# Ubuntu 22.04/24.04
+wget https://repo.radeon.com/amdgpu-install/7.0/ubuntu/jammy/amdgpu-install_7.0.60000-1_all.deb
+sudo apt install ./amdgpu-install_7.0.60000-1_all.deb
 sudo amdgpu-install --usecase=hiplibsdk,rocm
 
 # Add user to video group

diagnose_rocm.sh

@@ -1,7 +1,7 @@
 # Module 2: Advanced GPU Memory Management and Optimization
 *Mastering GPU Memory Hierarchies and Performance Optimization*
 
-> Environment note: Examples are tested in Docker containers with CUDA 12.9.1 and ROCm latest (rocm/dev-ubuntu-22.04:latest). Prefer Docker for reproducible builds.
+> Environment note: Examples are tested in Docker containers with CUDA 12.9.1 (Ubuntu 22.04) and ROCm 7.0 (Ubuntu 24.04). The improved build system automatically optimizes memory access patterns. Prefer Docker for reproducible builds.
 
 ## Learning Objectives
 After completing this module, you will be able to:

modules/module1/content.md

@@ -1,7 +1,7 @@
 # Module 3: Advanced GPU Algorithms and Parallel Patterns
 *Mastering High-Performance Parallel Computing Algorithms*
 
-> Environment note: Use the provided Docker images (CUDA 12.9.1, ROCm latest) for consistent toolchains across platforms.
+> Environment note: Use the provided Docker images (CUDA 12.9.1 on Ubuntu 22.04, ROCm 7.0 on Ubuntu 24.04) with automatic GPU detection for consistent toolchains across platforms.
 
 ## Learning Objectives
 After completing this module, you will be able to:

modules/module2/content.md

@@ -1,6 +1,6 @@
 # Module 4: Advanced GPU Programming - Multi-GPU, Streams, and Scalability
 
-> Environment note: Examples are validated with CUDA 12.9.1 and ROCm latest in Docker containers. Multi-GPU sections may require appropriate hardware and drivers.
+> Environment note: Examples are validated with CUDA 12.9.1 (Ubuntu 22.04) and ROCm 7.0 (Ubuntu 24.04) in Docker containers. Multi-GPU sections may require appropriate hardware and drivers. Auto-detection build system optimizes for your platform.
 
 ## Overview
 

modules/module3/content.md

@@ -1,6 +1,6 @@
 # Module 5: Performance Considerations and GPU Optimization
 
-> Environment note: Examples and profiling workflows are validated using Docker images with CUDA 12.9.1 and ROCm latest (rocm/dev-ubuntu-22.04:latest) for consistent toolchains.
+> Environment note: Examples and profiling workflows are validated using Docker images with CUDA 12.9.1 (Ubuntu 22.04) and ROCm 7.0 (Ubuntu 24.04) for consistent toolchains. Enhanced build system includes profiling integrations.
 
 ## Table of Contents
 1. [Introduction to GPU Performance Optimization](#introduction)

modules/module4/content.md

@@ -1,6 +1,6 @@
 # Module 6: Fundamental Parallel Algorithms - Comprehensive Guide
 
-> Environment note: The examples and benchmarks in this module are tested in Docker with CUDA 12.9.1 and ROCm latest to ensure reproducibility.
+> Environment note: The examples and benchmarks in this module are tested in Docker with CUDA 12.9.1 (Ubuntu 22.04) and ROCm 7.0 (Ubuntu 24.04) to ensure reproducibility. Recent algorithm fixes improve performance.
 
 ## Introduction
 

modules/module5/content.md

@@ -1,6 +1,6 @@
 # Module 7: Advanced Algorithmic Patterns - Comprehensive Guide
 
-> Environment note: Use the provided Docker environment (CUDA 12.9.1, ROCm latest) for consistent builds and tools across platforms.
+> Environment note: Use the provided Docker environment (CUDA 12.9.1 on Ubuntu 22.04, ROCm 7.0 on Ubuntu 24.04) for consistent builds and tools across platforms. Recent algorithmic pattern fixes included.
 
 ## Introduction