Parallel Computing
Processing multiple computations simultaneously rather than sequentially. Parallel computing is fundamental to AI training and inference, which involve massive matrix operations.
Why It Matters
Without parallel computing, training modern AI models would take years instead of weeks. It is the hardware paradigm that makes large-scale AI possible.
Example
8,000 GPU cores each computing a different part of a matrix multiplication simultaneously, completing in one step what would take 8,000 sequential steps on a CPU.
Think of it like...
Like having 1,000 workers building a wall at the same time instead of one worker laying every brick — massively faster for tasks that can be divided.
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.
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.
Distributed Training
Splitting model training across multiple GPUs or machines to handle larger models or datasets and reduce training time. Techniques include data parallelism and model parallelism.
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.