GPU Scheduling and Resource Management in Containerized Environments

GPU scheduling and resource management in containerized environments addresses the challenge of efficiently allocating and managing powerful GPU hardware among multiple containerized applications, particularly for AI/ML and high-performance computing workloads. Within orchestration systems like Kubernetes, this involves specialized device plugins and schedulers that discover available GPUs, advertise them as a schedulable resource, and implement policies to assign them to containers. Techniques range from dedicating whole GPUs to time-sharing or spatially partitioning them into smaller, isolated instances (e.g., using NVIDIA's Multi-Instance GPU technology), all with the ultimate goal of maximizing utilization, guaranteeing performance isolation, and ensuring fair and cost-effective access to these expensive accelerators.

1. Foundational Concepts
   1. The Role of GPUs in Modern Computing
      1. Evolution of GPU Usage
         1. Graphics Rendering Origins
         2. Transition to General-Purpose Computing
         3. GPGPU Programming Models
         4. Current Market Trends
      2. GPU Architecture Overview
         1. Streaming Multiprocessors
            1. SM Structure and Components
            2. Warp Scheduling
            3. Parallel Execution Model
            4. Resource Allocation within SMs
         2. CUDA Cores and Stream Processors
            1. SIMD Processing Architecture
            2. Arithmetic Logic Units
            3. Floating-Point Operations
            4. Integer Operations
         3. Tensor Cores
            1. AI/ML Acceleration Purpose
            2. Mixed-Precision Operations
            3. Matrix Operations
            4. Performance Benefits
         4. GPU Memory Hierarchy
            1. Global Memory
            2. Shared Memory
            3. L1 Cache
            4. L2 Cache
            5. Registers
            6. Constant Memory
            7. Texture Memory
            8. Memory Bandwidth Characteristics
            9. Memory Access Patterns
      3. Common GPU Workloads
         1. Artificial Intelligence and Machine Learning
            1. Deep Learning Training
            2. Model Inference
            3. Computer Vision
            4. Natural Language Processing
         2. High-Performance Computing
            1. Scientific Simulations
            2. Numerical Computation
            3. Molecular Dynamics
            4. Weather Modeling
         3. Data Analytics and Visualization
            1. Real-Time Data Processing
            2. 3D Rendering
            3. Video Processing
            4. Cryptocurrency Mining
   2. Containerization Fundamentals
      1. Core Container Concepts
         1. Container vs Virtual Machine
         2. Image vs Container Distinction
         3. Container Lifecycle Management
         4. Immutable Infrastructure Principles
      2. Linux Container Technologies
         1. Namespaces
            1. Process Namespace
            2. Network Namespace
            3. Mount Namespace
            4. User Namespace
            5. IPC Namespace
            6. UTS Namespace
         2. Control Groups
            1. CPU Control
            2. Memory Control
            3. Block I/O Control
            4. Network Control
            5. Device Control
         3. Union Filesystems
            1. OverlayFS
            2. AUFS
            3. Layer Management
      3. Container Runtimes
         1. Docker Engine
            1. Docker Daemon
            2. Docker CLI
            3. Image Management
            4. Container Networking
            5. Volume Management
         2. containerd
            1. Runtime Architecture
            2. Image Distribution
            3. Container Lifecycle
         3. CRI-O
            1. Kubernetes Integration
            2. OCI Compliance
      4. Container Orchestration Needs
         1. Multi-Container Applications
         2. Service Discovery
         3. Load Balancing
         4. Auto-scaling
         5. Health Monitoring
         6. Rolling Updates
   3. Container Orchestration with Kubernetes
      1. Kubernetes Architecture
         1. Control Plane Components
            1. API Server
            2. Scheduler
            3. Controller Manager
            4. etcd Key-Value Store
            5. Cloud Controller Manager
         2. Worker Node Components
            1. Kubelet
            2. Container Runtime
            3. Kube-proxy
            4. Node Agent DaemonSet
      2. Core Kubernetes Objects
         1. Pods
            1. Pod Specification
            2. Pod Lifecycle
            3. Multi-Container Pods
            4. Init Containers
            5. Sidecar Containers
         2. Deployments
            1. Replica Management
            2. Rolling Updates
            3. Rollback Strategies
            4. Deployment Strategies
         3. Services
            1. ClusterIP Service
            2. NodePort Service
            3. LoadBalancer Service
            4. ExternalName Service
            5. Headless Services
         4. ConfigMaps and Secrets
            1. Configuration Management
            2. Secret Management
            3. Volume Mounting
         5. DaemonSets
            1. Node Coverage
            2. System Services
            3. Monitoring Agents
         6. StatefulSets
            1. Persistent Storage
            2. Ordered Deployment
            3. Stable Network Identity
      3. Kubernetes Scheduling
         1. Scheduling Process Overview
         2. Resource Requests and Limits
         3. Node Selection Criteria
         4. Affinity and Anti-Affinity
         5. Taints and Tolerations
         6. Priority Classes