From Pixel to Parallel: Understanding Modern GPU Architecture

Practical Architectural Choices for Performance Optimization

Table of Contents

1. Introduction
2. GPU vs CPU: Architectural Differences
3. Physical Architecture of Modern GPUs
4. Memory Architecture
5. Computational Architecture
6. Practical Applications
7. Code Examples and Implementation
8. Process Flow and Architecture
9. Further Reading and Resources
10. Conclusion

1. Introduction

Modern graphics cards represent one of the most complex and powerful components in contemporary computing systems. To put their computational power into perspective, while the Nintendo 64 (1996) required about 100 million calculations per second, modern GPUs like those running Cyberpunk 2077 perform around 36 trillion calculations per second. To contextualize this astronomical number: if every person on Earth performed one calculation per second, we would need approximately 4,400 Earths to match a single modern GPU’s computational power.

Flow Architecture, Made via Mermaid (Code Written by me)

2. GPU vs CPU: Architectural Differences

The fundamental difference between GPUs and CPUs can be understood through an analogy:

• GPU = Cargo Ship Massive parallel processing capability Handles large volumes of similar calculations Limited flexibility in operation types Optimized for throughput Example: A modern GPU like GA102 contains over 10,000 cores
• CPU = Jumbo Jet Fewer but more powerful processing units Handles diverse types of calculations High flexibility and adaptability Optimized for quick response time Example: Modern CPUs typically have 8–24 cores

This architectural difference makes each processor type ideal for different tasks. CPUs excel at sequential processing and complex decision-making, while GPUs dominate in parallel processing scenarios.

3. Physical Architecture of Modern GPUs

GA102 GPU Analysis