Journal

[1]          T. Liu, N. Wolfe, C. D. Carothers, W. Ji, and X. G. Xu, “Optimizing the Monte Carlo neutron cross-section construction code, XSBench, for MIC and GPU platforms,” Nuclear Science and Engineering, vol. 185, pp. 232-242, 2017.

[2]          T. Liu, X. G. Xu, and C. D. Carothers, “Comparison of two accelerators for Monte Carlo radiation transport calculations, NVIDIA Tesla M2090 GPU and Intel Xeon Phi 5110p coprocessor: a case study for x-ray CT imaging dose calculation,” Annals of Nuclear Energy, vol. 82, pp. 230-239, 2015.

[3]          X. G. Xu, T. Liu, L. Su, X. Du, M. Riblett, W. Ji, et al., “ARCHER, a new Monte Carlo software tool for emerging heterogeneous computing environments,” Annals of Nuclear Energy, vol. 82, pp. 2-9, 2015.

[4]          L. Su, Y. Yang, B. Bednarz, E. Sterpin, X. Du, T. Liu, et al., “ARCHER-RT, A photon-electron coupled Monte Carlo dose computing engine for GPU:  software development and application to helical tomotherapy,” Medical Physics, vol. 41, p. 071709, 2014.

[5]          D. Zhang, A. Padole, X. Li, S. Singh, R. D. A. Khawaja, D. Lira, et al., “In vitro dose measurements in a human cadaver with abdomen/pelvis CT scans,” Medical Physics, vol. 41, p. 091911, 2014.

 

Conference

[1]          H. Lin, T. Liu, L. Su, C. Shi, X. Tang, D. Adam, et al., “Monte Carlo modeling and simulation of the Varian TrueBeam LINAC using heterogeneous computing,” Medical Physics, 2017.

[2]          T. Liu, H. Lin, B. Bednarz, C. Shi, X. Tang, and X. G. Xu, “Fast Monte Carlo source modeling and dose calculation for magnetic-resonance imaging-guided radiation therapy (MRIgRT),” presented at the 6th International Workshop on Computational Human Phantoms (CP2017), Annapolis, Maryland, USA, 2017.

[3]          T. Liu, H. Lin, L. Yang, H. Liu, Z. Wang, X. Pei, et al., “Fast dose calculation for magnetic-resonance imaging-guided radiation therapy (MRIgRT) using GPU-based Monte Carlo code ARCHER,” Medical Physics, 2017.

[4]          H. Lin, T. Liu, L. Su, B. Bednarz, P. Caracappa, and X. G. Xu, “Modeling of radiotherapy Linac source terms using ARCHER Monte Carlo code: performance comparison for GPU and MIC parallel computing devices,” in 13th International Conference on Radiation Shielding & 19th Topical Meeting of the Radiation Protection and Shielding Division (ICRS-13 & RPSD 2016), France, Paris, 2016.

[5]          T. Liu, H. Lin, Y. Gao, P. Caracappa, G. Wang, W. Cong, et al., “Radiation dose simulation for a newly proposed dynamic bowtie filters for CT using fast Monte Carlo methods,” Medical Physics, vol. 43, p. 3861, 2016.

[6]          T. Liu, H. Lin, L. Su, C. Shi, X. Tang, B. Bednarz, et al., “Modeling of radiotherapy Linac source terms using ARCHER Monte Carlo code: performance comparison of GPU and MIC computing accelerators,” Medical Physics, vol. 43, p. 3732, 2016.

[7]          T. Liu, N. Wolfe, H. Lin, K. Zieb, W. Ji, P. Caracappa, et al., “Performance study of Monte Carlo codes on Xeon Phi coprocessors — testing MCNP 6.1 and profiling ARCHER geometry module on the FS7ONNi problem,” in 13th International Conference on Radiation Shielding & 19th Topical Meeting of the Radiation Protection and Shielding Division (ICRS-13 & RPSD 2016), France, Paris, 2016.

[8]          T. Liu, H. Lin, P. F. Caracappa, and X. G. Xu, “Extension of a GPU/MIC based Monte Carlo Code, ARCHER, to internal radiation dose calculations,” Health Physics, vol. 109, p. S56, 2015.

[9]          T. Liu, H. Lin, X. G. Xu, and M. Stabin, “Development of a nuclear medicine dosimetry module for the GPU-Based Monte Carlo code ARCHER,” Medical Physics, vol. 42, p. 3661, 2015.

[10]        T. Liu, N. Wolfe, C. D. Carothers, and X. G. Xu, “Development of a medical physics Monte Carlo radiation transport code ARCHER,” in GPU Technology Conference 2015, San Jose, CA, USA, 2015.

[11]        T. Liu, N. Wolfe, L. Su, C. D. Carothers, B. Bednarz, and X. G. Xu, “Near real-time GPU and MIC-based Monte Carlo code ARCHER for radiation dose calculations in voxelized and mesh phantoms,” presented at the 5th International Workshop on Computational Human Phantoms (CP2015), Seoul, Korea, 2015.

[12]        N. Wolfe, C. D. Carothers, T. Liu, and X. G. Xu, “Concurrent CPU, GPU and MIC execution algorithms for ARCHER Monte Carlo code involving photon and neutron radiation transport problems,” in Joint International Conference on Mathematics and Computation (M&C), Supercomputing in Nuclear Applications (SNA) and the Monte Carlo (MC) Method (M&C+SNA+MC 2015), Nashville, TN, USA, 2015.

[13]        X. Du, T. Liu, L. Su, P. F. Caracappa, and X. G. Xu, “Extension of ARCHER Monte Carlo code to health physics dosimetry and shielding design: preliminary results,” Health Physics, vol. 107, p. S38, 2014.

[14]        X. Du, T. Liu, L. Su, W. Ji, P. F. Caracappa, and X. G. Xu, “Development of CSG-based radiation shielding module for ARCHER: preliminary results for photons,” in Radiation Protection and Shielding Division of the American Nuclear Society 2014, Knoxville, TN, USA, 2014.

[15]        H. Lin, T. Liu, L. Su, X. Du, Y. Gao, P. F. Caracappa, et al., “Formation of computational phantoms from CT numbers for use in the ARCHER Monte Carlo code,” Health Physics, vol. 107, p. S98, 2014.

[16]        T. Liu, X. Du, L. Su, Y. Gao, W. Ji, D. Zhang, et al., “Testing of ARCHER-CT, a fast Monte Carlo Code for CT dose calculation: experiment versus simulation,” Transactions of the American Nuclear Society, vol. 110, p. 481, 2014.

[17]        T. Liu, X. Du, L. Su, Y. Gao, W. Ji, D. Zhang, et al., “Monte Carlo CT dose calculation: a comparison between experiment and simulation using ARCHER-CT,” Medical Physics, vol. 41, p. 424, 2014.

[18]        T. Liu, X. Du, L. Su, W. Ji, and X. G. Xu, “Development of ARCHER-CT, a fast Monte Carlo code for patient-specific CT dose calculations using Nvidia GPU and Intel coprocessor technologies,” in GPU Technology Conference 2014, San Jose, CA, USA, 2014.

[19]        T. Liu, L. Su, X. Du, P. F. Caracappa, and X. G. Xu, “Comparison of accuracy and speed of ARCHER with MCNP for organ dose calculations from external photon beams under standard irradiation geometries,” Health Physics, vol. 107, p. S114, 2014.

[20]        T. Liu, L. Su, X. Du, H. Lin, K. Zieb, W. Ji, et al., “Parallel Monte Carlo methods for heterogeneous hardware computer systems using GPUs and coprocessors: recent development of ARCHER code,” in Radiation Protection and Shielding Division of the American Nuclear Society 2014, Knoxville, TN, USA, 2014.

[21]        N. Wolfe, T. Liu, C. Carothers, and X. G. Xu, “Heterogeneous concurrent execution of Monte Carlo photon transport on CPU, GPU and MIC,” in Proceedings of the 4th Workshop on Irregular Applications: Architectures and Algorithms, 2014, pp. 49-52.

[22]        X. Du, T. Liu, W. Ji, X. G. Xu, and F. B. Brown, “Evaluation of vectorized Monte Carlo algorithms on GPUs for a neutron eigenvalue problem,” in Proceedings of International Conference on Mathematics and Computational Methods Applied to Nuclear Science & Engineering (M&C 2013), Sun Valley, Idaho, USA, 2013, pp. 2513-2522.

[23]        X. Du, T. Liu, L. Su, M. Riblett, and X. G. Xu, “A hardware accelerator based fast Monte Carlo code for radiation dosimetry: software design and preliminary results,” Medical Physics, vol. 40, p. 475, 2013.

[24]        T. Liu, X. Du, W. Ji, X. G. Xu, and F. B. Brown, “A comparative study of history-based versus vectorized Monte Carlo methods in the GPU/CUDA environment for a simple neutron eigenvalue problem,” in Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo (SNA & MC 2013), Paris, France, 2013.

[25]        T. Liu, X. Du, and X. G. Xu, “Affordable supercomputer-based Monte Carlo CT dose calculations: a hardware comparison between Nvidia M2090 GPU and Intel Xeon Phi 5110p coprocessor,” Medical Physics, vol. 40, p. 459, 2013.

[26]        T. Liu, W. Ji, and X. G. Xu, “Development of GPU-based Monte Carlo code for fast CT imaging dose calculation on CUDA Fermi architecture,” in International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 13), Sun Valley, ID, 2013.

[27]        T. Liu, X. G. Xu, and C. D. Carothers, “Comparison of two accelerators for Monte Carlo radiation transport calculations, NVIDIA Tesla M2090 GPU and Intel Xeon Phi 5110p coprocessor: a case study for x-ray CT imaging dose calculation,” in Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo (SNA & MC 2013), Paris, France, 2013.

[28]        L. Su, X. Du, T. Liu, and X. G. Xu, “GPU-accelerated Monte Carlo electron transport methods: development and application for radiation dose calculations using six GPU cards,” in Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo (SNA & MC 2013), Paris, France, 2013.

[29]        L. Su, X. Du, T. Liu, and X. G. Xu, “Fast Monte Carlo electron-photon transport code using hardware accelerators: preliminary results for brachytherapy and radionuclide therapy cases,” Medical Physics, vol. 40, p. 397, 2013.

[30]        X. G. Xu, T. Liu, L. Su, X. Du, M. Riblett, W. Ji, et al., “An update of ARCHER, a Monte Carlo radiation transport software testbed for emerging hardware such as GPUs,” Transactions of the American Nuclear Society, vol. 108, pp. 433-434, 2013.

[31]        X. G. Xu, T. Liu, L. Su, X. Du, M. J. Riblett, W. Ji, et al., “ARCHER, a new Monte Carlo software tool for emerging heterogeneous computing environments,” in Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo (SNA & MC 2013), Paris, France, 2013.

[32]        T. Liu, A. Ding, W. Ji, X. G. Xu, C. D. Carothers, and F. B. Brown, “A Monte Carlo neutron transport code for eigenvalue calculations on a dual-GPU system and CUDA environment,” in International Topical Meeting on Advances in Reactor Physics (PHYSOR 2012), Knoxville, TN, USA, 2012.

[33]        T. Liu, A. Ding, and X. G. Xu, “Accelerated Monte Carlo methods for photon dosimetry using a dual-GPU system and CUDA,” Medical Physics, vol. 39, p. 3818, 2012.

[34]        T. Liu, A. Ding, and X. G. Xu, “GPU-based Monte Carlo methods for accelerating radiographic and CT imaging dose calculations: feasibility and scalability,” Medical Physics, vol. 39, p. 3876, 2012.

[35]        T. Liu, L. Su, A. Ding, W. Ji, C. D. Carothers, and X. G. Xu, “GPU/CUDA-ready parallel Monte Carlo codes for reactor analysis and other applications,” Transactions of the American Nuclear Society, vol. 106, pp. 378-379, 2012.

[36]        L. Su, T. Liu, A. Ding, and X. G. Xu, “A GPU/CUDA based Monte Carlo code for proton transport: preliminary results of proton depth dose in water,” Medical Physics, vol. 39, p. 3945, 2012 2012.

[37]        A. Ding, T. Liu, C. Liang, W. Ji, M. S. Shepard, X. G. Xu, et al., “Evaluation of speedup of Monte Carlo calculations of simple reactor physics problems coded for the GPU/CUDA environment,” in International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 11), Rio de Janeiro, Brazil, 2011.