Benchmarking and preparing LPSim for scalability on multiple GPUs

This paper presents a scaling methodology for benchmarking the performance of traffic simulation algorithms on multiple GPUs with different hardware specifications and the naive implementation of GPU-based traffic simulation with multi-GPU machines. The methodology aims to enable fair comparisons of runtime across different devices and assess how a program scales with the number of cores in the GPU. The study includes a comprehensive evaluation of a traffic simulation algorithm implemented on multiple graphical processing units (GPUs), comparing its performance with a single-GPU implementation. The results demonstrate that the multi-GPU implementation outperforms the single-GPU implementation in terms of sim- ulation speed, highlighting the benefits of implementing traffic simulations on multi-GPU architectures. The findings provide insights for designing more efficient and scalable traffic simulation algorithms, contributing to improved traffic management and congestion reduction on roads. The paper also discusses the use of Bulk Synchronous Parallel (BSP) modeling, normalizing runtime across different GPU hardware, and the application of Amdahl’s law to assess speedup and optimize parallelization. Future work includes optimizing inter-GPU communication and load balancing for further scalability improvements.