Analyzing scalability of parallel algorithms and architectures
Journal of Parallel and Distributed Computing - Special issue on scalability of parallel algorithms and architectures
An Asynchronous Parallel Supernodal Algorithm for Sparse Gaussian Elimination
SIAM Journal on Matrix Analysis and Applications
Efficient large-scale power grid analysis based on preconditioned krylov-subspace iterative methods
Proceedings of the 38th annual Design Automation Conference
Isoefficiency: Measuring the Scalability of Parallel Algorithms and Architectures
IEEE Parallel & Distributed Technology: Systems & Technology
Parallel domain decomposition for simulation of large-scale power grids
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Multigrid on GPU: tackling power grid analysis on parallel SIMT platforms
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Parallel Power Grid Simulation on Platforms with Multi Core Processors
ICC '09 Proceedings of the 2009 International Conference on Computing, Engineering and Information
Power grid analysis using random walks
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Design and Implementation of Block-Based Partitioning for Parallel Flip-Chip Power-Grid Analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Scalable power grid transient analysis via MOR-assisted time-domain simulations
Proceedings of the International Conference on Computer-Aided Design
Parallel power grid analysis using preconditioned GMRES solver on CPU-GPU platforms
Proceedings of the International Conference on Computer-Aided Design
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Due to the rapid advances of integrated circuit technology, the size of power distribution network (power grid) is becoming larger and larger. There are usually multi-million nodes on a power grid. Analyzing these huge power grids has become very expensive in terms of both time and memory. This paper presents an efficient parallel implementation of the Additive Schwarz Method (ASM) for IR-drop analysis of large-scale power grid. Based on distributed memory system, a new data storage method is proposed to overcome memory bottleneck of traditional methods. Techniques including overlapping in multiple layer and irregular power grid, via detection and grouping are utilized to accelerate the simulation. Moreover, a new communication strategy exhibiting minimum communication overhead is proposed. The proposed method is very accurate in the final solution, with the maximum error less than 0.1mv. Experimental results on industrial medium size benchmarks show that the proposed method achieves more than 110X speedup over a state-of-the-art direct LU solver. The proposed approach can easily solve very large-scale benchmarks, while LU solver fails to obtain the solution because of system memory limitation. It is the first time reported in literature that IR-drop analysis of power grid with over 190M nodes is successfully solved within 5 minutes.