Where GPUs Fit

GPUs fit workloads that can keep many execution lanes busy with similar work: dense linear algebra, graphics, stencil computations, simulation kernels, batched inference, training steps, and other jobs where data parallelism is large enough to amortize scheduling and memory movement.

When the kernel is regular, the hardware can turn parallelism into throughput. That is the real advantage.

Where They Fight The Program

The fit gets worse when a program depends on heavy branching, irregular pointer chasing, scattered writes, tight serial dependencies, or one thread producing a large amount of state that other lanes cannot help with.

Those constraints do not make GPUs weak. They make them specific. A CPU, FPGA, ASIC, distributed actor system, or mixed architecture may be the better shape when locality, control flow, latency, or authority boundaries matter more than raw parallel throughput.