CPUsGPUs

CPUs and GPUs are central components in modern computer systems. A CPU is designed for general-purpose computing, handling diverse tasks with complex control flow. A GPU is optimized for high-throughput parallel workloads, excelling at processing large data sets with simple operations across many cores.

CPU architecture emphasizes low latency per instruction. CPUs typically have a small number of powerful cores, large caches, advanced branch prediction, and strong single-thread performance. They are well suited to operating systems, serial code, and workloads with irregular memory access.

GPUs feature many simpler cores organized for simultaneous execution of the same instructions on different data. They provide very high memory bandwidth and a different memory hierarchy with shared and global memory. Programming models include CUDA, OpenCL, and vendor tools; GPUs are used for graphics, scientific simulations, deep learning, and other data-parallel tasks.

Integration and heterogeneity: Many modern CPUs include an integrated GPU, while discrete GPUs attach via PCIe or high-speed interconnects. Heterogeneous computing combines CPU and GPU execution, with data transfer and memory management playing a central role in performance optimization.

Trends: The distinction between CPU and GPU workloads continues to blur as accelerators and unified memory technologies grow. Performance is judged by throughput, latency, FLOPS, bandwidth, and energy efficiency, with software increasingly designed to offload parallelizable tasks to GPUs.