High Bandwidth Memory (HBM)
High Bandwidth Memory (HBM) architecture: 3D-stacked DRAM with TSV technology powering NVIDIA GPUs and AI accelerators with TB/s bandwidth.
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GPU Computing
Graphics processing units and parallel computing concepts.
14
Concepts
All GPU Computing Concepts
GPU Memory Hierarchy & Optimization
Master GPU memory hierarchy from registers to global memory, understand coalescing patterns, bank conflicts, and optimization strategies for maximum performance
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Multi-GPU Communication: NVLink vs PCIe, NCCL, and Distributed Training
Compare NVLink vs PCIe bandwidth for multi-GPU training. Learn GPU topologies, NVSwitch, and choose between NCCL, Gloo, and MPI for distributed deep learning.
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NVIDIA Unified Virtual Memory
NVIDIA Unified Virtual Memory (UVM): on-demand page migration, memory oversubscription, and simplified CPU-GPU memory management.
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Page Migration & Fault Handling
CUDA page migration and fault handling between CPU and GPU memory. Learn TLB management, DMA transfers, and memory optimization.
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NVIDIA Device Files in /dev/
Understanding character devices, major/minor numbers, and the device file hierarchy created by NVIDIA drivers for GPU access in Linux.
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Distributed Parallelism in Deep Learning
GPU distributed parallelism: Data Parallel (DDP), Tensor Parallel, Pipeline Parallel, and ZeRO optimization for training large AI models.
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CUDA Multi-Process Service (MPS)
Learn CUDA Multi-Process Service (MPS) for GPU sharing. Enable concurrent kernel execution from multiple processes and maximize GPU utilization.
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Understanding NVIDIA Kubernetes GPU Operator
Automate NVIDIA GPU management in Kubernetes with the GPU Operator. Deploy drivers, device plugins, and monitoring as DaemonSets.
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Understanding CUDA Contexts
Explore the concept of CUDA contexts, their role in managing GPU resources, and how they enable parallel execution across multiple CPU threads.
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Understanding NVIDIA Persistence Daemon
Eliminating GPU initialization latency through nvidia-persistenced - a userspace daemon that maintains GPU driver state for optimal startup performance.
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NCCL: High-Performance Multi-GPU Communication
Master NVIDIA NCCL for multi-GPU deep learning. Learn AllReduce, ring algorithms, and GPU-Direct communication for efficient distributed training on CUDA.
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Tensor Cores: Accelerating Deep Learning
NVIDIA Tensor Cores explained: mixed-precision matrix operations delivering 10x speedups for AI training and inference on CUDA GPUs.
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GPU Streaming Multiprocessor (SM)
Deep dive into the fundamental processing unit of modern GPUs - the Streaming Multiprocessor architecture, execution model, and memory hierarchy
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