Accelerated Compute

Overview

Traditional computing relies on CPUs to execute instructions one after another. Accelerated computing offloads the parallelizable parts of a workload to specialized processors, most commonly GPUs, that run thousands of operations at once. For AI, scientific simulation, and large-scale analytics, this parallelism delivers speedups of ten, a hundred, or several hundred times over CPU-only execution.

How it works

An accelerated system pairs a CPU, which handles general logic, control flow, and coordination, with one or more GPUs that carry the heavy mathematical lifting. The CPU dispatches parallel work to the GPUs, collects the results, and manages the rest of the program. Programming models such as CUDA expose this division of labor to developers through familiar languages and libraries.

Why it matters

The mathematics underpinning AI, dense matrix multiplications, tensor operations, and gradient calculations, is inherently parallel and maps almost perfectly onto GPU hardware. Attempting the same work on CPUs alone would be orders of magnitude slower and prohibitively expensive. Without accelerated computing, training and serving models at today's scale would simply not be practical.