From c6c8f6e07282549d1dde28410e4a3962ad163063 Mon Sep 17 00:00:00 2001 From: Stephen Shao Date: Sun, 21 Sep 2025 19:38:52 -0400 Subject: [PATCH] Updated the docs in the project --- README.md | 40 +++++++++---------- modules/module3/README.md | 2 +- modules/module5/README.md | 4 +- modules/module6/README.md | 2 +- modules/module7/README.md | 2 +- modules/module8/README.md | 8 ++-- .../module8/examples/01_deep_learning_cuda.cu | 2 +- .../module8/examples/01_deep_learning_hip.cpp | 2 +- modules/module8/examples/Makefile | 6 +-- modules/module9/README.md | 4 +- modules/module9/content.md | 6 +-- .../module9/examples/01_architecture_cuda.cu | 8 ++-- .../module9/examples/01_architecture_hip.cpp | 6 +-- modules/module9/examples/Makefile | 4 +- 14 files changed, 48 insertions(+), 48 deletions(-) diff --git a/README.md b/README.md index 5c2eb62..a833a65 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ **A comprehensive, hands-on educational project for mastering GPU programming with CUDA and HIP** -*From beginner fundamentals to production-ready optimization techniques* +*From beginner fundamentals to professional-grade optimization techniques* ## πŸ“‘ Table of Contents @@ -37,7 +37,7 @@ - **9 comprehensive modules** covering beginner to expert topics - **71 working code examples** in both CUDA and HIP - **Cross-platform support** for NVIDIA and AMD GPUs -- **Production-ready development environment** with Docker +- **Comprehensive development environment** with Docker - **Professional tooling** including profilers, debuggers, and CI/CD Perfect for students, researchers, and developers looking to master GPU computing. @@ -198,7 +198,7 @@ This architectural knowledge is essential for writing efficient GPU code and is | 🎯 **Complete Curriculum** | 9 progressive modules from basics to advanced topics | | πŸ’» **Cross-Platform** | Full CUDA and HIP support for NVIDIA and AMD GPUs | | 🐳 **Docker Ready** | Complete containerized development environment with CUDA 12.9.1 & ROCm 7.0 | -| πŸ”§ **Production Quality** | Professional build systems, auto-detection, testing, and profiling | +| πŸ”§ **Professional Quality** | Professional build systems, auto-detection, testing, and profiling | | πŸ“Š **Performance Focus** | Optimization techniques and benchmarking throughout | | 🌐 **Community Driven** | Open source with comprehensive contribution guidelines | | πŸ§ͺ **Advanced Libraries** | Support for Thrust, MIOpen, and production ML frameworks | @@ -252,21 +252,21 @@ Choose your track based on your experience level: ## πŸ“š Modules -Our comprehensive curriculum progresses from fundamental concepts to production-ready optimization techniques: +Our comprehensive curriculum progresses from fundamental concepts to advanced optimization techniques: -| Module | Level | Duration | Focus Area | Key Topics | Examples | -|--------|-------|----------|------------|------------|----------| -| [**Module 1**](modules/module1/) | πŸ‘Ά Beginner | 4-6h | **GPU Fundamentals** | Architecture, Memory, First Kernels | 13 | -| [**Module 2**](modules/module2/) | πŸ‘Άβ†’πŸ”₯ | 6-8h | **Memory Optimization** | Coalescing, Shared Memory, Texture | 10 | -| [**Module 3**](modules/module3/) | πŸ”₯ Intermediate | 6-8h | **Execution Models** | Warps, Occupancy, Synchronization | 12 | -| [**Module 4**](modules/module4/) | πŸ”₯β†’πŸš€ | 8-10h | **Advanced Programming** | Streams, Multi-GPU, Unified Memory | 9 | -| [**Module 5**](modules/module5/) | πŸš€ Advanced | 6-8h | **Performance Engineering** | Profiling, Bottleneck Analysis | 5 | -| [**Module 6**](modules/module6/) | πŸš€ Advanced | 8-10h | **Parallel Algorithms** | Reduction, Scan, Convolution | 10 | -| [**Module 7**](modules/module7/) | πŸš€ Expert | 8-10h | **Algorithmic Patterns** | Sorting, Graph Algorithms | 4 | -| [**Module 8**](modules/module8/) | πŸš€ Expert | 10-12h | **Domain Applications** | ML, Scientific Computing | 4 | -| [**Module 9**](modules/module9/) | πŸš€ Expert | 6-8h | **Production Deployment** | Libraries, Integration, Scaling | 4 | +| Module | Level | Focus Area | Key Topics | Examples | +|--------|-------|------------|------------|----------| +| [**Module 1**](modules/module1/) | πŸ‘Ά Beginner | **GPU Fundamentals** | Architecture, Memory, First Kernels | 13 | +| [**Module 2**](modules/module2/) | πŸ‘Άβ†’πŸ”₯ | **Memory Optimization** | Coalescing, Shared Memory, Texture | 10 | +| [**Module 3**](modules/module3/) | πŸ”₯ Intermediate | **Execution Models** | Warps, Occupancy, Synchronization | 12 | +| [**Module 4**](modules/module4/) | πŸ”₯β†’πŸš€ | **Advanced Programming** | Streams, Multi-GPU, Unified Memory | 9 | +| [**Module 5**](modules/module5/) | πŸš€ Advanced | **Performance Engineering** | Profiling, Bottleneck Analysis | 5 | +| [**Module 6**](modules/module6/) | πŸš€ Advanced | **Parallel Algorithms** | Reduction, Scan, Convolution | 10 | +| [**Module 7**](modules/module7/) | πŸš€ Expert | **Algorithmic Patterns** | Sorting, Graph Algorithms | 4 | +| [**Module 8**](modules/module8/) | πŸš€ Expert | **Domain Applications** | ML, Scientific Computing | 4 | +| [**Module 9**](modules/module9/) | πŸš€ Expert | **Production Deployment** | Libraries, Integration, Scaling | 4 | -**πŸ“ˆ Progressive Learning Path: 71 Examples β€’ 50+ Hours β€’ Beginner to Expert** +**πŸ“ˆ Progressive Learning Path: 71 Examples β€’ Beginner to Expert** ### Learning Progression @@ -387,7 +387,7 @@ Experience the full development environment with zero setup: **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 +- **Libraries**: Professional toolchains with debugging support **[πŸ“– Complete Docker Guide β†’](docker/README.md)** @@ -415,7 +415,7 @@ 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 +- **Professional 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 @@ -426,7 +426,7 @@ make debug # Debug builds with extra checks |--------------|-------------------|------------------|--------------| | **Beginner** | 10-100x | 60-80% | Educational | | **Intermediate** | 50-500x | 80-95% | Optimized | -| **Advanced** | 100-1000x | 85-95% | Production | +| **Advanced** | 100-1000x | 85-95% | Professional | | **Expert** | 500-5000x | 95%+ | Library-Quality | ## πŸ› Troubleshooting @@ -507,7 +507,7 @@ If you use this project in your research, education, or publications, please cit author={{Stephen Shao}}, year={2025}, howpublished={\url{https://github.com/AIComputing101/gpu-programming-101}}, - note={A complete GPU programming educational resource with 70+ production-ready examples covering fundamentals through advanced optimization techniques for NVIDIA CUDA and AMD HIP platforms} + note={A complete GPU programming educational resource with 71 comprehensive examples covering fundamentals through advanced optimization techniques for NVIDIA CUDA and AMD HIP platforms} } ``` diff --git a/modules/module3/README.md b/modules/module3/README.md index 0bfb368..983f4f9 100644 --- a/modules/module3/README.md +++ b/modules/module3/README.md @@ -334,4 +334,4 @@ ncu --metrics l1tex__data_bank_conflicts_pipe_lsu_mem_shared_op_ld.sum ./02_scan --- -**Note**: This module provides both educational implementations (showing algorithm progression) and production-ready optimized versions. Focus on understanding the concepts before optimizing for specific use cases. \ No newline at end of file +**Note**: This module provides both educational implementations (showing algorithm progression) and optimized versions. Focus on understanding the concepts before optimizing for specific use cases. \ No newline at end of file diff --git a/modules/module5/README.md b/modules/module5/README.md index b2a8d93..75f1af3 100644 --- a/modules/module5/README.md +++ b/modules/module5/README.md @@ -436,11 +436,11 @@ Module 5 represents the pinnacle of GPU performance optimization, covering: - **Memory Subsystem Optimization** across all levels of the GPU memory hierarchy - **Compute Optimization Strategies** for maximum algorithmic efficiency - **Cross-Platform Performance** considerations for portable high-performance code -- **Production-Ready Optimization** techniques used in industry applications +- **Professional Optimization** techniques used in industry applications These skills are essential for: - Achieving maximum performance from GPU investments -- Building production-quality high-performance applications +- Building professional-quality high-performance applications - Understanding performance trade-offs in GPU algorithm design - Developing performance-portable code across GPU architectures diff --git a/modules/module6/README.md b/modules/module6/README.md index 2570cc7..269d349 100644 --- a/modules/module6/README.md +++ b/modules/module6/README.md @@ -367,4 +367,4 @@ These algorithms form the foundation for more complex applications covered in su **Difficulty**: Intermediate-Advanced **Prerequisites**: Modules 1-5 completion, parallel algorithm concepts -**Note**: This module emphasizes both educational understanding and production-ready implementations. Focus on mastering the algorithmic concepts before diving into platform-specific optimizations. \ No newline at end of file +**Note**: This module emphasizes both educational understanding and optimized implementations. Focus on mastering the algorithmic concepts before diving into platform-specific optimizations. \ No newline at end of file diff --git a/modules/module7/README.md b/modules/module7/README.md index 858ec28..71db8be 100644 --- a/modules/module7/README.md +++ b/modules/module7/README.md @@ -372,4 +372,4 @@ Master these concepts to tackle the most demanding computational challenges and **Difficulty**: Advanced **Prerequisites**: Modules 1-6 completion, advanced algorithm knowledge -**Note**: This module focuses on production-level implementations of sophisticated algorithms. Emphasis is placed on understanding both the theoretical foundations and practical optimization techniques required for real-world deployment. \ No newline at end of file +**Note**: This module focuses on advanced-level implementations of sophisticated algorithms. Emphasis is placed on understanding both the theoretical foundations and practical optimization techniques required for real-world deployment. \ No newline at end of file diff --git a/modules/module8/README.md b/modules/module8/README.md index 29bbf07..b84f48d 100644 --- a/modules/module8/README.md +++ b/modules/module8/README.md @@ -30,7 +30,7 @@ By completing this module, you will: #### 1. Deep Learning Inference Kernels (`01_deep_learning_*.cu/.cpp`) -Production-quality neural network inference implementations: +Professional-quality neural network inference implementations: - **Custom Convolution Kernels**: Optimized for specific layer configurations - **GEMM Optimization**: High-performance matrix multiplication for fully connected layers @@ -214,7 +214,7 @@ make monte_carlo # Monte Carlo simulations make finance # Computational finance make library_integration # Library integration examples -# Production builds with optimizations +# Professional builds with optimizations make production # Debug builds for development @@ -396,7 +396,7 @@ make scaling_analysis Module 8 bridges the gap between GPU programming techniques and real-world applications: - **Domain Expertise**: Apply GPU techniques to solve actual industry problems -- **Production Quality**: Build applications that meet real-world performance and accuracy requirements +- **Professional Quality**: Build applications that meet real-world performance and accuracy requirements - **Integration Skills**: Successfully integrate GPU computing into existing workflows and systems - **Optimization Mastery**: Achieve optimal performance for domain-specific computational patterns @@ -414,4 +414,4 @@ Master these domain-specific applications to become a complete GPU computing exp **Difficulty**: Advanced **Prerequisites**: Modules 1-7 completion, domain-specific knowledge -**Note**: This module emphasizes real-world application development with production-quality implementations. Students should focus on both technical excellence and practical deployment considerations. \ No newline at end of file +**Note**: This module emphasizes real-world application development with professional-quality implementations. Students should focus on both technical excellence and practical deployment considerations. \ No newline at end of file diff --git a/modules/module8/examples/01_deep_learning_cuda.cu b/modules/module8/examples/01_deep_learning_cuda.cu index 2358c4c..575869e 100644 --- a/modules/module8/examples/01_deep_learning_cuda.cu +++ b/modules/module8/examples/01_deep_learning_cuda.cu @@ -1,7 +1,7 @@ /** * Module 8: Domain-Specific Applications - Deep Learning Inference Kernels (CUDA) * - * Production-quality neural network inference implementations optimized for NVIDIA GPU architectures. + * Professional-quality neural network inference implementations optimized for NVIDIA GPU architectures. * This example demonstrates custom convolution kernels, GEMM optimization, activation functions, * and mixed precision inference with Tensor Core utilization. * diff --git a/modules/module8/examples/01_deep_learning_hip.cpp b/modules/module8/examples/01_deep_learning_hip.cpp index 00e890d..8fd3e74 100644 --- a/modules/module8/examples/01_deep_learning_hip.cpp +++ b/modules/module8/examples/01_deep_learning_hip.cpp @@ -13,7 +13,7 @@ const int WAVEFRONT_SIZE = 64;earning Inference Kernels (HIP) * - * Production-quality neural network inference implementations optimized for AMD GPU architectures. + * Professional-quality neural network inference implementations optimized for AMD GPU architectures. * This example demonstrates deep learning kernels adapted for ROCm/HIP with wavefront-aware * optimizations and LDS utilization patterns specific to AMD hardware. * diff --git a/modules/module8/examples/Makefile b/modules/module8/examples/Makefile index 0cd6a55..d68b85f 100644 --- a/modules/module8/examples/Makefile +++ b/modules/module8/examples/Makefile @@ -24,7 +24,7 @@ BUILD_HIP = 0 GPU_VENDOR = NONE endif -# Compiler flags for production-quality applications +# Compiler flags for professional-quality applications CUDA_FLAGS = -std=c++17 -O3 -arch=sm_70 -lineinfo --use_fast_math CUDA_DEBUG_FLAGS = -std=c++17 -g -G -arch=sm_70 HIP_FLAGS = -std=c++17 -O3 -ffast-math @@ -186,7 +186,7 @@ debug: CUDA_FLAGS = $(CUDA_DEBUG_FLAGS) debug: HIP_FLAGS = $(HIP_DEBUG_FLAGS) debug: all -# Production builds with maximum optimization +# Professional builds with maximum optimization .PHONY: production production: CUDA_FLAGS += -DNDEBUG -Xptxas -O3 production: HIP_FLAGS += -DNDEBUG @@ -589,7 +589,7 @@ help: @echo " cuda - Build CUDA applications only" @echo " hip - Build HIP applications only" @echo " debug - Build with debug flags" - @echo " production - Build with maximum optimization" + @echo " professional - Build with maximum optimization" @echo " clean - Remove build artifacts" @echo "" @echo "Domain Application Targets:" diff --git a/modules/module9/README.md b/modules/module9/README.md index 6805b90..7144e33 100644 --- a/modules/module9/README.md +++ b/modules/module9/README.md @@ -1,6 +1,6 @@ # Module 9: Production GPU Programming -This module focuses on building enterprise-grade GPU applications with emphasis on deployment, maintenance, scalability, and integration with production systems. Learn how to transition from prototype to production-ready GPU software. +This module focuses on building enterprise-grade GPU applications with emphasis on deployment, maintenance, scalability, and integration with production systems. Learn how to transition from prototype to professional-grade GPU software. ## Learning Objectives @@ -357,7 +357,7 @@ make cost_analysis - [ ] Monitoring and observability built into the application ### Infrastructure -- [ ] Production-grade Kubernetes cluster with GPU support +- [ ] Enterprise-grade Kubernetes cluster with GPU support - [ ] Monitoring and alerting infrastructure deployed - [ ] Backup and disaster recovery procedures implemented - [ ] Security scanning and vulnerability management in place diff --git a/modules/module9/content.md b/modules/module9/content.md index e46f1ef..6179894 100644 --- a/modules/module9/content.md +++ b/modules/module9/content.md @@ -1,6 +1,6 @@ -# Production GPU Programming: Enterprise-Grade Implementation Guide +# Professional GPU Programming: Enterprise-Grade Implementation Guide -> Environment note: Production examples and deployment references assume development using Docker images with CUDA 12.9.1 (Ubuntu 22.04) and ROCm 7.0 (Ubuntu 24.04) for parity between environments. Enhanced build system supports production-grade optimizations. +> Environment note: Professional examples and deployment references assume development using Docker images with CUDA 12.9.1 (Ubuntu 22.04) and ROCm 7.0 (Ubuntu 24.04) for parity between environments. Enhanced build system supports professional-grade optimizations. This comprehensive guide covers all aspects of deploying, maintaining, and scaling GPU applications in production environments, from architecture design to operational excellence. @@ -1469,6 +1469,6 @@ This comprehensive guide covers all essential aspects of production GPU programm 4. **Monitoring**: Comprehensive observability for GPU workloads 5. **Scalability**: Auto-scaling and load balancing strategies 6. **Security**: Enterprise-grade security and compliance -7. **Best Practices**: Production-ready configuration and health monitoring +7. **Best Practices**: Professional configuration and health monitoring These concepts enable the development of enterprise-grade GPU applications that meet the demanding requirements of production environments while maintaining high performance, reliability, and security standards. \ No newline at end of file diff --git a/modules/module9/examples/01_architecture_cuda.cu b/modules/module9/examples/01_architecture_cuda.cu index b551604..5005d8e 100644 --- a/modules/module9/examples/01_architecture_cuda.cu +++ b/modules/module9/examples/01_architecture_cuda.cu @@ -1,12 +1,12 @@ /** * Module 9: Production GPU Programming - Production Architecture Patterns (CUDA) * - * Enterprise-grade GPU application architecture demonstrating production-ready patterns + * Enterprise-grade GPU application architecture demonstrating professional patterns * including microservices design, error handling, monitoring integration, and scalable * deployment strategies. This example showcases real-world production requirements. * * Topics Covered: - * - Production-grade error handling and recovery mechanisms + * - Professional-grade error handling and recovery mechanisms * - Comprehensive logging and monitoring integration * - Resource management and memory pools * - Health checks and service discovery integration @@ -37,7 +37,7 @@ #include #include -// Production-grade error handling macros +// Professional-grade error handling macros #define CUDA_CHECK_PROD(call, context) \ do { \ cudaError_t error = call; \ @@ -841,7 +841,7 @@ int main(int argc, char* argv[]) { // Demo mode - show capabilities std::cout << "Production GPU Architecture Features:\n"; std::cout << "β€’ Comprehensive error handling and recovery\n"; - std::cout << "β€’ Production-grade logging and monitoring\n"; + std::cout << "β€’ Professional-grade logging and monitoring\n"; std::cout << "β€’ Resource management and memory pools\n"; std::cout << "β€’ Health checks and service discovery\n"; std::cout << "β€’ Configuration management\n"; diff --git a/modules/module9/examples/01_architecture_hip.cpp b/modules/module9/examples/01_architecture_hip.cpp index 52a0840..07aecd6 100644 --- a/modules/module9/examples/01_architecture_hip.cpp +++ b/modules/module9/examples/01_architecture_hip.cpp @@ -1,7 +1,7 @@ /** * Module 9: Production GPU Programming - Production Architecture Patterns (HIP) * - * Enterprise-grade GPU application architecture demonstrating production-ready patterns + * Enterprise-grade GPU application architecture demonstrating professional patterns * adapted for AMD GPU architectures using ROCm/HIP. This example showcases real-world * production requirements optimized for AMD hardware and ROCm ecosystem. * @@ -34,7 +34,7 @@ #include #include -// Production-grade error handling macros for HIP +// Professional-grade error handling macros for HIP #define HIP_CHECK_PROD(call, context) \ do { \ hipError_t error = call; \ @@ -828,7 +828,7 @@ int main(int argc, char* argv[]) { std::cout << "β€’ Wavefront-aware resource management (64-thread wavefronts)\n"; std::cout << "β€’ NUMA-aware memory allocation for multi-GPU systems\n"; std::cout << "β€’ AMD GPU specific error handling and recovery\n"; - std::cout << "β€’ Production-grade logging optimized for ROCm ecosystem\n"; + std::cout << "β€’ Professional-grade logging optimized for ROCm ecosystem\n"; std::cout << "β€’ Multi-tenant resource isolation for AMD GPUs\n"; std::cout << "β€’ Real-time health monitoring with AMD-specific thresholds\n"; diff --git a/modules/module9/examples/Makefile b/modules/module9/examples/Makefile index 68785f9..32bb553 100644 --- a/modules/module9/examples/Makefile +++ b/modules/module9/examples/Makefile @@ -25,7 +25,7 @@ BUILD_HIP = 0 GPU_VENDOR = NONE endif -# Compiler flags for production-ready applications +# Compiler flags for professional applications CUDA_FLAGS = -std=c++17 -O3 -arch=sm_70 -lineinfo --use_fast_math -DPRODUCTION_BUILD CUDA_DEBUG_FLAGS = -std=c++17 -g -G -arch=sm_70 -DDEBUG_BUILD HIP_FLAGS = -std=c++17 -O3 -ffast-math -DPRODUCTION_BUILD @@ -659,7 +659,7 @@ help: @echo "" @echo "Build Targets:" @echo " all - Build all production applications" - @echo " production - Build with production optimization and hardening" + @echo " professional - Build with professional optimization and hardening" @echo " debug - Build with debug information" @echo " clean - Remove all build artifacts" @echo ""