Offload

Offload refers to the practice of transferring a portion of a system's workload from a general-purpose processor to a specialized device or subsystem that can perform it more efficiently. The goal is to improve performance, reduce energy consumption, or free CPU cycles for other tasks.

Domains include computing, networking, and storage. In computing, offload occurs when compute tasks are executed by accelerators such as GPUs, FPGAs, or ASICs rather than the CPU. Common tasks include graphics rendering, machine learning inference, cryptographic operations, and data compression.

Network offload uses specialized hardware—such as network interface cards with TCP offload, RDMA, or data processing units (DPUs)—to handle packet processing, segmentation, encryption, and policy enforcement.

Storage offload includes controllers or adapters that handle I/O scheduling, RAID, caching, deduplication, compression, or replication without returning data to the CPU.

Architecture and implementation: hardware offload is often realized through PCIe cards, System-on-Chip units, or DPUs; software offload uses kernel bypass, user-space networks, or offload engines; virtualization can use SR-IOV to partition hardware.

Benefits include reduced CPU load, lower latency, higher throughput, and improved energy efficiency.

Trade-offs: increased hardware cost, complexity, potential vendor lock-in, and challenges with debugging and portability.

Applications include databases and analytics, AI inference pipelines, video encoding, network security appliances, cloud storage gateways, and high-performance computing.

Oftentimes, offload decisions are guided by workload characteristics, data locality, and cost-benefit analyses.