A GPU-Enabled Extension for Apache Ignite to Facilitate Running Genetic Algorithms

Beni, Majid Salimi; Sojoodi, Amir Hossein; Khunjush, Farshad

doi:10.1109/cads50570.2020.9211857

Cited by 1 publication

(1 citation statement)

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“…A parallel GA on GPU for vehicle routing is proposed in [31], where a speedup of 1700x is achieved when compared to a sequential implementation on CPU. Other implementations of the GA on GPU are published in [32][33][34]. In all instances, data-level parallelism must be exploited in the algorithm to allow the implementation to benefit from the massively parallel architecture of the GPU.…”

Section: Previous Workmentioning

confidence: 99%

Parallel Algorithm on Multicore Processor and Graphics Processing Unit for the Optimization of Electric Vehicle Recharge Scheduling

Roberge,

Brooks,

Tarbouchi

2024

Electronics

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Electric vehicles (EVs) are becoming more and more popular as they provide significant environmental benefits compared to fossil-fuel vehicles. However, they represent substantial loads on the power grid, and the scheduling of EV charging can be a challenge, especially in large parking lots. This paper presents a metaheuristic-based approach parallelized on multicore processors (CPU) and graphics processing units (GPU) to optimize the scheduling of EV charging in a single smart parking lot. The proposed method uses a particle swarm optimization algorithm that takes as input the arrival time, the departure time, and the power demand of the vehicles and produces an optimized charging schedule for all vehicles in the parking lot, which minimizes the overall charging cost while respecting the chargers’ capacity and the parking lot feeder capacity. The algorithm exploits task-level parallelism for the multicore CPU implementation and data-level parallelism for the GPU implementation. The proposed algorithm is tested in simulation on parking lots containing 20 to 500 EVs. The parallel implementation on CPUs provides a speedup of 7.1x, while the implementation on a GPU provides a speedup of up to 247.6x. The parallel implementation on a GPU is able to optimize the charging schedule for a 20-EV parking lot in 0.87 s and a 500-EV lot in just under 30 s. These runtimes allow for real-time computation when a vehicle arrives at the parking lot or when the electricity cost profile changes.

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