Beginner

Introduction to GPU Programming for AI

GPUs are the engines of modern AI. Understanding GPU architecture and programming is essential for anyone building or optimizing deep learning systems.

Why GPUs for AI?

Deep learning involves massive matrix multiplications and element-wise operations on tensors with millions of elements. GPUs excel at these tasks because they have thousands of small cores designed for parallel computation, compared to CPUs which have a few powerful cores optimized for sequential tasks.

A single NVIDIA H100 GPU can perform over 1,000 TFLOPS of FP16 computation — roughly 100x faster than a high-end CPU for matrix operations.

CPU vs GPU Architecture

FeatureCPUGPU
Cores8-64 powerful coresThousands of simple cores (e.g., 16,896 on H100)
Clock Speed3-5 GHz1-2 GHz
Memory64-512 GB DDR524-80 GB HBM3
Bandwidth~100 GB/s~3,000 GB/s (HBM3)
Best ForSequential, branching logicParallel, uniform computation

The GPU Computing Stack

  1. Hardware

    GPU silicon with Streaming Multiprocessors (SMs), Tensor Cores, and High Bandwidth Memory (HBM).

  2. Drivers & Runtime

    NVIDIA drivers and CUDA runtime that interface with the hardware.

  3. Libraries

    cuBLAS (linear algebra), cuDNN (deep learning primitives), cuFFT (FFT), and NCCL (multi-GPU communication).

  4. Frameworks

    PyTorch, TensorFlow, and JAX that provide high-level APIs using these libraries under the hood.

  5. Applications

    Training and inference of neural networks, scientific computing, and data processing.

Key Concepts

  • Parallelism: Running thousands of operations simultaneously. A matrix multiplication of two 4096x4096 matrices involves ~137 billion operations — GPUs execute these in parallel.
  • Memory hierarchy: GPU memory has multiple levels (registers, shared memory, L2 cache, global memory) with different speeds and sizes. Optimizing data movement is crucial.
  • Throughput vs Latency: GPUs optimize for throughput (operations per second) rather than latency (time for a single operation). Individual operations may be slower, but processing millions in parallel gives higher total throughput.
  • Tensor Cores: Specialized hardware units on modern NVIDIA GPUs that accelerate matrix multiply-accumulate operations, the core of deep learning.

Getting Started

Bash - Check GPU Setup 
# Check NVIDIA driver and GPU info
nvidia-smi

# Check CUDA version
nvcc --version

# Verify PyTorch sees the GPU
python -c "import torch; print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))"
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Key takeaway: GPUs accelerate AI by executing thousands of parallel operations simultaneously. Understanding the GPU architecture, memory hierarchy, and computing stack is the foundation for writing efficient GPU code and optimizing deep learning workloads.