The invention discloses a low-energy-consumption task scheduling strategy for CPU-GPU heterogeneity, and provides an
ant colony task scheduling
algorithm capable of simultaneously paying attention toreal-time constraints and
system energy consumption aiming at the characteristics of a heterogeneous multi-
core system and the problems that a traditional
ant colony
algorithm only optimizes a singletarget and is too slow in convergence speed. The method comprises the following steps: firstly, providing guidance information in a
pheromone initialization process according to the energy consumptionof a task on a heterogeneous core; accelerating
algorithm convergence speed, then, the cores are screened through task real-time constraint conditions; the method comprises the steps of obtaining a heterogeneous core of a task, selecting an appropriate execution core according to the calculation
energy consumption of the task on the heterogeneous core, the inter-core communication
energy consumption of different tasks and the
pheromone content, continuously searching a scheduling scheme with lower energy consumption through multiple iterations of an
ant colony algorithm, and adjusting the
pheromone content according to the obtained result to further accelerate the convergence speed of the algorithm. A final task scheduling scheme is obtained after a plurality of iterations, so that the energy consumption of the
system can be optimized under the condition that the real-time constraint of the task is met.