INTRODUCTION TO CUDA PLATFORM AND SYSTEM
SETUP
Overview of CUDA and GPU Architecture
What is CUDA and how it fits into GPU computing
GPU vs CPU for parallel processing
Understanding the NVIDIA ecosystem: drivers, CUDA toolkit,
cuDNN
Installing and Verifying CUDA on Linux
Installing NVIDIA drivers and CUDA toolkit
Verifying installation with nvidia-smi and sample builds
Setting up environment variables and PATH configuration
GPU Monitoring and Device Management
Using nvidia-smi to monitor GPU stats
Configuring power states, fan settings, and persistence
mode
Checking compatibility and runtime issues
ADVANCED CONFIGURATION AND MULTI-GPU
MANAGEMENT
Multi-GPU and Resource Management
Identifying and isolating GPUs for specific processes
Managing multi-GPU workloads
Using MIG (Multi-Instance GPU) on supported hardware
CUDA Toolkit Management and Versioning
Supporting multiple CUDA versions
Managing library dependencies and symbolic links
Using modules and environment managers (e.g., Conda)
CONTAINERIZATION, MONITORING, AND
TROUBLESHOOTING
CUDA in Containers and Virtualized
Environments
Using Docker with NVIDIA Container Toolkit
Setting up GPU access in containers
Managing container images with CUDA dependencies
Troubleshooting and Performance
Optimization
Diagnosing driver and kernel issues
Handling runtime errors, segmentation faults, and memory
leaks
Profiling and optimizing resource usage
Final Hands-On and Wrap-Up
Deploy a containerized CUDA environment
Configure and monitor a multi-GPU workload
Review of common issues and admin best practices
