CUDA
Compute Unified Device Architecture — NVIDIA's parallel computing platform that enables GPU programming for AI workloads. CUDA is the dominant software ecosystem for AI computation.
Why It Matters
CUDA's near-monopoly on AI software is why NVIDIA GPUs dominate AI. Most ML frameworks are built on CUDA, creating a powerful ecosystem lock-in.
Example
Writing CUDA kernels that execute matrix multiplications across thousands of GPU cores simultaneously, achieving 100x speedups over CPU implementations.
Think of it like...
Like the operating system for AI hardware — just as Windows made PCs useful, CUDA made GPUs useful for AI.
Related Terms
GPU
Graphics Processing Unit — originally designed for rendering graphics, GPUs excel at the parallel mathematical operations needed for training and running AI models. They are the primary hardware for modern AI.
Parallel Computing
Processing multiple computations simultaneously rather than sequentially. Parallel computing is fundamental to AI training and inference, which involve massive matrix operations.
Compute
The computational resources (processing power, memory, time) required to train or run AI models. Compute is measured in FLOPs (floating-point operations) and is a primary constraint and cost in AI development.
Hardware Acceleration
Using specialized hardware (GPUs, TPUs, FPGAs, ASICs) to speed up AI computation compared to general-purpose CPUs. Accelerators are optimized for the specific math operations used in neural networks.