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CUDA-based Parallel Bi-Conjugate Gradient Matrix Solver for BioFET Simulation  

Park, Tae-Jung (Computer Graphics Lab., Dept. of Computer Science, Korea University)
Woo, Jun-Myung (Dept. of Electrical Engineering, Seoul National University)
Kim, Chang-Hun (Computer Graphics Lab., Dept. of Computer Science, Korea University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.48, no.1, 2011 , pp. 90-100 More about this Journal
Abstract
We present a parallel bi-conjugate gradient (Bi-CG) matrix solver for large scale Bio-FET simulations based on recent graphics processing units (GPUs) which can realize a large-scale parallel processing with very low cost. The proposed method is focused on solving the Poisson equation in a parallel way, which requires massive computational resources in not only semiconductor simulation, but also other various fields including computational fluid dynamics and heat transfer simulations. As a result, our solver is around 30 times faster than those with traditional methods based on single core CPU systems in solving the Possion equation in a 3D FDM (Finite Difference Method) scheme. The proposed method is implemented and tested based on NVIDIA's CUDA (Compute Unified Device Architecture) environment which enables general purpose parallel processing in GPUs. Unlike other similar GPU-based approaches which apply usually 32-bit single-precision floating point arithmetics, we use 64-bit double-precision operations for better convergence. Applications on the CUDA platform are rather easy to implement but very hard to get optimized performances. In this regard, we also discuss the optimization strategy of the proposed method.
Keywords
CUDA; GPGPU; Bi-CG;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Y. Liu, K. Lilja, C. Heitzinger, and R. W. Dutton, "Overcoming the screening-induced performance limits of nanowire biosensors: a simulation study on the effect of electro-diffusion flow," in IEDM Tech. Dig., San Francisco, pp. 491-494, 2008.
2 C. Heitzinger, N. J. Mauser, and C. Ringhofer, Multiscale Modeling of Planar and Nanowire Field-Effect Biosensors, SIAM J. Appl. Math. Volume 70, Issue 5, pp. 1634-1654, 2010.   DOI   ScienceOn
3 NVIDIA, CUDA C Programming guide. Version 3.1.1, July, 2010. http://developer.download.nvidia.com/compute/cuda/3_1/toolkit/docs/NVIDIA_CUDA_C_ProgrammingGuide_3.1.pdf
4 이호영, 박종현, 김준성, "CUDA를 이용한 FDTD 알고리즘의 병렬처리," 전자공학회논문지, 제47권 CI편, 제4호, 82-87쪽, 2010년 7월
5 이주석, 류현곤, "GPU 병렬 컴퓨팅 기술을 이용한 개인용 수퍼 컴퓨터 현황과 전망 - CUDA 기술에 대한 이해," 전자공학회논문지, 제36권, 제5호, 18-27쪽, 2009년 5월
6 G. Strang, Computational Science and Engineering, Wellesley-Cambridge Press, pp. 586-595, 2007.
7 F. V'azquez, G. Ortega, J.J. Fern'andez, E.M. Garz'on. Improving the performance of the sparse matrix vector product with GPUs, In Proceedings of the 10th IEEE International Conference on Computer and Information Technology (CIT 2010), pp. 1146-1151. Bradford, the UK, July, 2010.
8 F. V'azquez, E.M. Garzon, J.A. Martinez, and J.J. Fernandez. Accelerating sparse matrix vector product with GPUs. In Proceedings of the 2009 International Conference on Computational and Mathematical Methods in Science and Engineering, volume 2, pp. 1081-1092. CMMSE, 2009.
9 C. Ericson, Real Time Collison Detection, Morgan Kaufmann Publishers, pp 525-536, 2005.
10 X. L. Wu, N. Obeid, and W. M. Hwu, Exploiting More Parallelism from Applications Having Gneralized Reduction on GPU Architectures, In Proceedings of the 10th IEEE International Conference on Computer and Information Technology (CIT 2010), pp 1175-1180, Bradford, the UK, July, 2010.
11 M. Harris, Optimizing Parallel Reduction in CUDA. http://developer.download.nvidia.com/compute/cuda/1_1/Website/projects/reduction/doc/reduction.pdf
12 D. Landheer, G. Aers, W. R. McKinnon, M. J. Deen, and J. C. Ranuarez, "Model for the field effect from layers of biological macromolecules on the gates of metal-oxide-semiconductor transistors," Journal of Applied Physics, vol. 98, p. 044701-044701-15, 2005.   DOI   ScienceOn
13 Y. Cui, Q. Wei, H. Park, C. M. Lieber, Nanowire Nanosensors for Highly Sensitive and Selective Detection of Biological and Chemical Species, Science 293, 1289, 2001.   DOI   ScienceOn
14 C. Stagni, C. Guiducci, L. Benini, B. Riccò, S. Carrara, B. Samorí, C. Paulus, M. Schienle, M. Augustyniak, and R. Thewes, CMOS DNA Sensor Array With Integrated A/D Conversion Based on Label-Free Capacitance Measurement, IEEE Journal of Solid-State Circuits 41, 2956, 2006.   DOI