DAC '97 Proceedings of the 34th annual Design Automation Conference
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
Sparse matrix storage revisited
Proceedings of the 2nd conference on Computing frontiers
SBCCI '06 Proceedings of the 19th annual symposium on Integrated circuits and systems design
3D-Vias Aware Quadratic Placement for 3D VLSI Circuits
ISVLSI '07 Proceedings of the IEEE Computer Society Annual Symposium on VLSI
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Parallel multi-level analytical global placement on graphics processing units
Proceedings of the 2009 International Conference on Computer-Aided Design
Efficient fault simulation on many-core processors
Proceedings of the 47th Design Automation Conference
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Graphics Processing Units (GPUs) can be viewed as stream processors and, therefore, can be applied to improve the performance of data-parallel algorithms. GPUs can beat CPUs in most stream-like algorithms and have been successfully applied to solve problem in areas such as biology, audio and image processing, database queries and others. This paper presents a VLSI cell placement tool running on a GPU in order to show the viability of applying graphics hardware to improve the performance of CAD tools. Our results show that GPU versions of linear algebra algorithms run 3x or more faster than CPU versions.