CPU vs GPU Architecture: A Deep Dive into Design, Performance, and Applications
Table of Contents Introduction Fundamental Design Goals 2.1 What a CPU Is Built For 2.2 What a GPU Is Built For CPU Architecture Explained 3.1 Core Pipeline Stages 3.2 Cache Hierarchy 3.3 Branch Prediction & Out‑of‑Order Execution 3.4 Instruction Set Architectures (ISAs) GPU Architecture Explained 4.1 Streaming Multiprocessors (SMs) 4.2 SIMD / SIMT Execution Model 4.3 Memory Sub‑systems: Global, Shared, and Registers 4.4 Specialized Units (Tensor Cores, Ray‑Tracing) Head‑to‑Head Comparison 5.1 Latency vs. Throughput 5.2 Parallelism Granularity 5.3 Power Efficiency 5.4 Programming Model Differences Real‑World Workloads and Use Cases 6.1 General‑Purpose Computing (GPGPU) 6.2 Graphics Rendering Pipeline 6.3 Machine Learning & AI 6.4 High‑Performance Computing (HPC) Practical Code Examples 7.1 CPU Parallelism with OpenMP 7.2 GPU Parallelism with CUDA Future Trends and Convergence 8.1 Heterogeneous Computing Platforms 8.2 Architectural Innovations (e.g., AMD CDNA, Intel Xe‑HPG) 8.3 Software Ecosystem Evolution Conclusion Resources Introduction When you power on a modern computer, two distinct silicon engines typically start humming: the Central Processing Unit (CPU) and the Graphics Processing Unit (GPU). Though both are processors, they embody fundamentally different design philosophies, hardware structures, and performance characteristics. Understanding these differences is essential for software engineers, system architects, data scientists, and anyone who wants to extract the most value from today’s heterogeneous computing platforms. ...