Description
High-performance image, signal, and array-processing pipelines can be written with a language that separates algorithm intent from scheduling decisions.
It is useful for developers optimizing data-parallel computation across CPUs, GPUs, and different platforms. The learning curve is technical, and performance depends on correct schedules, target hardware, compiler settings, and representative benchmarks.