Larrabee: a many-core x86 architecture for visual computing
ACM SIGGRAPH 2008 papers
Towards acceleration of fault simulation using graphics processing units
Proceedings of the 45th annual Design Automation Conference
Multigrid on GPU: tackling power grid analysis on parallel SIMT platforms
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Accelerating statistical static timing analysis using graphics processing units
Proceedings of the 2009 Asia and South Pacific Design Automation Conference
Fast circuit simulation on graphics processing units
Proceedings of the 2009 Asia and South Pacific Design Automation Conference
Implementing survey propagation on graphics processing units
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Fast thermal simulation of 2D/3D integrated circuits exploiting neural networks and GPUs
Proceedings of the 17th IEEE/ACM international symposium on Low-power electronics and design
GPU programming for EDA with OpenCL
Proceedings of the International Conference on Computer-Aided Design
Exploring high throughput computing paradigm for global routing
Proceedings of the International Conference on Computer-Aided Design
Accelerating thermal simulations of 3D ICs with liquid cooling using neural networks
Proceedings of the great lakes symposium on VLSI
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Advances in GPU technology have propelled the GPU into arenas far afield from the traditional, isolated roles they have previously played. With hundreds of processing units in a single GPU, substantial speedups can be achieved by harnessing their power to augment the performance of the traditional single- or multi-core CPU on certain compute-intensive applications. However, utilizing the GPU requires both a change in the programmer's traditional algorithmic model as well as a judicious selection of algorithm being used for the problem. This paper reviews the GPU architecture and the tools available to utilize this valuable resource. It also provides insight into the type of problem best suited for the GPU as well as programming styles required to fully harness the power of the GPU. We present examples of specific EDA algorithms that can benefit from GPU acceleration, using both the CUDA and OpenCL environments.