CPUbound

CPU-bound describes a class of workloads in which the primary limiting factor on running time is the speed of the central processing unit (CPU). In such tasks, computation dominates the time spent during execution, and waiting for external resources is comparatively negligible.

I/O-bound tasks, by contrast, spend most of their time waiting for input/output operations—disk access, network transfers, or other peripheral interactions—so their execution slows mainly due to wait times rather than CPU cycles. A program can be CPU-bound in one phase and IO-bound in another.

Identifying CPU-bound behavior usually involves profiling and metrics: high CPU utilization, short I/O wait, long wall-clock time with abundant compute, and relatively small delays from I/O. Tools and techniques include runtime profilers and tracing, CPU cycle measurements, and examining bottlenecks such as algorithmic complexity or inefficient loops. Amdahl's law provides a theoretical bound on speedups from parallelism in CPU-bound workloads.

Optimization strategies focus on increasing effective CPU work per unit time. This can include choosing faster algorithms or improving asymptotic complexity, optimizing code for cache locality and vectorization, and parallelizing across multiple cores or using SIMD. In practice, developers may also employ asynchronous I/O to overlap computation with waiting or use libraries that release language runtime limitations (for example, using native extensions or multiprocessing to bypass a single-threaded bottleneck).

Note that some workloads alternate between CPU-bound and IO-bound phases, or are constrained by hardware resources such as memory bandwidth, caches, or virtualization overhead, complicating simple classifications. Understanding the bottleneck helps decision-making about optimizations, hardware upgrades, or architectural changes.