As integrated circuit design continues to scale, three-dimensional integrated circuit (3D-IC) technology has emerged as a promising solution to extend Moore’s Law. Among various physical design stages, placement plays a pivotal role, directly influencing key power, performance, and area (PPA) metrics. In research that does not consider specific 3DIC process technologies, the state-of-the-art ePlace-3D introduces 3D electrostatic field density modeling, enabling nonlinear placement algorithms to truly address 3D placement problems. However, its complex flow results in low runtime efficiency. In this work, we propose LightPlace, a lightweight and effective 3D placement framework. This framework incorporates Virtual Macro Insertion approach and Macro Connection Reconstruction method, which streamline the macro placement phase and allocate space for subsequent standard cell placement. These innovations enhance runtime efficiency and optimize wirelength. Experimental results on the large-scale Modern Mixed-Size (MMS) benchmarks demonstrate that, LightPlace reduces total wirelength by an average of 10% and achieves up to 4× faster runtime compared to the runtime reported for ePlace-3D.

Abstract Distributed computing frameworks play a crucial role in supporting compute-intensive applications in the era of big data. The growing demand for computing resources has spurred the interconnection of data centers, leading to the formation of supercomputing Internet. MapReduce is a popular distributed computing framework designed for large independent clusters. The original MapReduce framework deployed on supercomputing Internet performs inefficiently due to redundant geo-distributed reduce operations. Nonetheless, its abstraction remains significant potential. This paper proposes an enhanced MapReduce framework for geo-distributed supercomputing Internet to minimize the necessity for data transmission across data centers. Leveraging hierarchical scheduling techniques, the framework optimizes data locality to mitigate network latency and bandwidth consumption during reduce operations, thereby reducing overall job execution times. The paper introduces a mathematical model for task scheduling within supercomputing Internet and formally describes the data transmission process among data centers. In the job scheduling phase, our framework facilitates efficient overlap of transferring and computing through pre-selected data centers. Meanwhile, in the data transmission phase, the framework aggregate data to reduce the frequency of transmission, thus alleviating the adverse effects on transmission of hierarchical network architecture. Comparative analysis with existing methods demonstrates the efficacy of the proposed framework in addressing similar computational challenges. Empirical evaluations underscore the effectiveness of our method in practice.