GPU Overview: Principles, Use Cases, Limitations, and Market Landscape

GPUs originated as specialized co‑processors for high‑parallelism image rendering, offering high throughput and tolerance for latency. When CPUs cannot meet specific performance demands, GPUs provide targeted acceleration for graphics and other parallel workloads.

The article defines GPUs, lists alternative names (display core, visual processor), and explains their primary function in image display pipelines, where the CPU determines content and the GPU determines quality.

Two deployment models are discussed: discrete GPUs, which deliver higher graphics performance at greater cost, power, and heat, and integrated GPUs, common in mobile platforms where they share resources with the CPU on a System‑on‑Chip (SoC).

Why co‑processors are needed is illustrated by six performance dimensions—accuracy, parallelism, latency, throughput, interaction complexity, and real‑time requirements. No single chip can optimize all dimensions simultaneously, so GPUs fill the niche of high parallelism and throughput for graphics‑centric tasks.

The article examines what GPUs can do beyond graphics, such as scientific simulation, financial calculations, search, and data mining, and why they are unsuitable for tasks with heavy branching, serial components, or mesh‑structured data flows (e.g., certain FFT workloads).

Current market offerings are highlighted: Nvidia’s proprietary CUDA platform and AMD’s OpenCL‑based solutions, both providing compilers that break workloads into parallel threads and modest hardware tweaks to improve latency and interaction performance.

Architecturally, GPUs allocate most resources to floating‑point multiply‑add units and employ a simple tree‑structured Network‑on‑Chip (NoC). This topology minimizes resource usage but can become a bottleneck under heavy data traffic, limiting suitability for many large‑scale parallel applications.

Finally, the article notes that while GPUs dominate graphics markets, their applicability to broader parallel computing depends on the uniqueness of the target workload’s performance radar; tasks that diverge significantly from graphics requirements may be better served by other accelerators such as DSPs, FPGAs, or specialized CPUs.