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Modelling and Simulation in Engineering
Volume 2013 (2013), Article ID 475478, 16 pages
http://dx.doi.org/10.1155/2013/475478
Research Article

Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA

Received 15 October 2012; Revised 12 February 2013; Accepted 19 February 2013

Academic Editor: Weizhong Dai

Copyright © 2013 Anuj V. Prakash et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. A. Cundall and O. D. L. Strack, “A discrete numerical model for granular assemblies,” Geotechnique, vol. 29, no. 1, pp. 47–65, 1979. View at Google Scholar · View at Scopus
  2. H. B. Matthews, S. M. Miller, and J. B. Rawlings, “Model identification for crystallization: theory and experimental verification,” Powder Technology, vol. 88, no. 3, pp. 227–235, 1996. View at Publisher · View at Google Scholar · View at Scopus
  3. R. Ramachandran, C. D. Immanuel, F. Stepanek, J. D. Litster, and F. J. Doyle, “A mechanistic model for breakage in population balances of granulation: theoretical kernel development and experimental validation,” Chemical Engineering Research and Design, vol. 87, no. 4, pp. 598–614, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. F. J. Muzzio, T. Shinbrot, and B. J. Glasser, “Powder technology in the pharmaceutical industry: the need to catch up fast,” Powder Technology, vol. 124, no. 1-2, pp. 1–7, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. S. M. Iveson, J. D. Litster, K. Hapgood, and B. J. Ennis, “Nucleation, growth and breakage phenomena in agitated wet granulation processes: a review,” Powder Technology, vol. 117, no. 1-2, pp. 3–39, 2001. View at Publisher · View at Google Scholar · View at Scopus
  6. J. A. Gantt, I. T. Cameron, J. D. Litster, and E. P. Gatzke, “Determination of coalescence kernels for high-shear granulation using DEM simulations,” Powder Technology, vol. 170, no. 2, pp. 53–63, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. C. D. Immanuel and F. J. Doyle III, “Solution technique for a multi-dimensional population balance model describing granulation processes,” Powder Technology, vol. 156, no. 2-3, pp. 213–225, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. J. M. H. Poon, C. D. Immanuel, F. J. Doyle, III F.J., and J. D. Litster, “A three-dimensional population balance model of granulation with a mechanistic representation of the nucleation and aggregation phenomena,” Chemical Engineering Science, vol. 63, no. 5, pp. 1315–1329, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. J. M. H. Poon, R. Ramachandran, C. F. W. Sanders et al., “Experimental validation studies on a multi-dimensional and multi-scale population balance model of batch granulation,” Chemical Engineering Science, vol. 64, no. 4, pp. 775–786, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Dosta, S. Heinrich, and J. Werther, “Fluidized bed spray granulation: analysis of the system behaviour by means of dynamic flowsheet simulation,” Powder Technology, vol. 204, no. 1, pp. 71–82, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. R. Ramachandran and P. I. Barton, “Effective parameter estimation within a multi-dimensional population balance model framework,” Chemical Engineering Science, vol. 65, no. 16, pp. 4884–4893, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Ramachandran and A. Chaudhury, “Model-based design and control of continuous drum granulation processes,” Chemical Engineering Research & Design, vol. 90, no. 8, pp. 1063–1073, 2012. View at Publisher · View at Google Scholar
  13. R. Ramachandran, M. A. Ansari, A. Chaudhury, A. Kapadia, A. V. Prakash, and F. Stepanek, “A quantitative assessment of the influence of primary particle size distribution on granule inhomogeneity,” Chemical Engineering Science, vol. 71, pp. 104–110. View at Publisher · View at Google Scholar
  14. J. A. Gantt and E. P. Gatzke, “A stochastic technique for multidimensional granulation modeling,” AIChE Journal, vol. 52, no. 9, pp. 3067–3077, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. F. Stepanek, P. Rajniak, C. Mancinelli, R. T. Chern, and R. Ramachandran, “Distribution and accessibility of binder in wet granules,” Powder Technology, vol. 189, no. 2, pp. 376–384, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. P. Rajniak, F. Stepanek, K. Dhanasekharan, R. Fan, C. Mancinelli, and R. T. Chern, “A combined experimental and computational study of wet granulation in a Wurster fluid bed granulator,” Powder Technology, vol. 189, no. 2, pp. 190–201, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. B. Freireich, J. Li, J. Litster, and C. Wassgren, “Incorporating particle flow information from discrete element simulations in population balance models of mixer-coaters,” Chemical Engineering Science, vol. 66, no. 16, pp. 3592–3604, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. R. Ramachandran, J. Arjunan, A. Chaudhury, and M. Ierapetritou, “Model-based control-loop performance assessment of a continuous direct compaction pharmaceutical process,” Journal of Pharmaceutical Innovation, vol. 6, pp. 249–263, 2011. View at Google Scholar
  19. P. G. Raeth and J. C. Chaves, “Parallel matlab using standard mpi implementations,” in Proceedings of the High Performance Computing Modernization Program Users Group Conference (HPCMP-UGC '10), pp. 438–441, June 2010.
  20. R. Panuganti, A high productivity framework for parallel data intensive computing in MATLAB [Ph.D. thesis], The Ohio State University, 2009.
  21. R. Swinburne, How to Overclock the Intel Core I5-2500k, 2011.
  22. M. Papadrakakis, G. Stavroulakis, and A. Karatarakis, “A new era in scientific computing: domain decomposition methods in hybrid CPU-GPU architectures,” Computer Methods in Applied Mechanics and Engineering, vol. 200, no. 13–16, pp. 1490–1508, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. Z. Bai-Da, T. Yu-Hua, W. Jun-Jie, and L. Xin, “Speeding up the matlab complex networks package using graphic processors,” Chinese Physics B, vol. 20, Article ID 098901, 2011. View at Google Scholar
  24. R. Gunawan, I. Fusman, and R. D. Braatz, “Parallel high-resolution finite volume simulation of particulate processes,” AIChE Journal, vol. 54, no. 6, pp. 1449–1458, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Ganesan and L. Tobiska, “An operator-splitting finite element method for the effcient parallel solution of multidimensional population balance systems,” Chemical Engineering Science, vol. 69, no. 1, pp. 59–68, 2012. View at Google Scholar
  26. C. A. Radeke, B. J. Glasser, and J. G. Khinast, “Large-scale powder mixer simulations using massively parallel GPUarchitectures,” Chemical Engineering Science, vol. 65, no. 24, pp. 6435–6442, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. P. C. Kapur, “A coalescence model for granulation,” Iandec Process Design and Development, vol. 8, no. 1, pp. 51–56, 1969. View at Google Scholar · View at Scopus
  28. A. D. Salman, M. J. Hounslow, and J. P. K. Seville, “Preface,” Handbook of Powder Technology, vol. 11, pp. xi–xii, 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Annapragada and J. Neilly, “On the modelling of granulation processes: a short note,” Powder Technology, vol. 89, no. 1, pp. 83–84, 1996. View at Publisher · View at Google Scholar · View at Scopus
  30. S. M. Iveson, “Limitations of one-dimensional population balance models of wet granulation processes,” Powder Technology, vol. 124, no. 3, pp. 219–229, 2002. View at Publisher · View at Google Scholar · View at Scopus
  31. D. Verkoeijen, G. A. Pouw, G. M. H. Meesters, and B. Scarlett, “Population balances for particulate processes—a volume approach,” Chemical Engineering Science, vol. 57, no. 12, pp. 2287–2303, 2002. View at Publisher · View at Google Scholar · View at Scopus
  32. D. Ramkrishna, Population Balances, Theory an Applications to Particulate Systems Engineering, Elsevier Science, 2000.
  33. R. Buyya, The Design of Paras Microkernel, 1998.
  34. ORACLE, “An oracle white paper: parallel programming with oracle developer tools,” Tech. Rep., Oracle Corporation, Redwood Shores, Calif, USA, 2010. View at Google Scholar
  35. SPEC, Third Quarter 2011 SPEC CPU2006 Results, Standard Performance Evaluation Corporation, Gainesville, Fla, USA, 2011.
  36. B. Wilkinson and M. Allen, Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers, Prentice Hall, 1st edition, 1999.
  37. A. Grama, A. Gupta, G. Karypis, and V. Kumar, Introduction to Parallel Computing, Addison Wesley, 2nd edition, 2003.
  38. I. Foster, Designing and Building Parallel Programs, Addison Wesley, 1st edition, 1995.
  39. B. Barney, Introduction to Parallel Computing, 2011.
  40. W. Gao and Q. Kemao, “Parallel computing in experimental mechanics and optical measurement: a review,” Optics and Lasers in Engineering, vol. 50, no. 4, pp. 608–617, 2011. View at Google Scholar
  41. R. Duncan, “Survey of parallel computer architectures,” Computer, vol. 23, no. 2, pp. 5–16, 1990. View at Publisher · View at Google Scholar · View at Scopus
  42. S. Siewert, “Using intel streaming simd extensions and intel integrated performance primitives to accelerate algorithms,” Tech. Rep., Atrato, 2009. View at Google Scholar
  43. MathWorks, Parallel Computing Toolbox: Product Description, MathWorks, Natick, Mass, USA, 2011.
  44. P. Luszczek, “Parallel programming in MATLAB,” International Journal of High Performance Computing Applications, vol. 23, no. 3, pp. 277–283, 2009. View at Publisher · View at Google Scholar · View at Scopus
  45. F. Warg, Techniques to reduce thread-level speculation overhead [Ph.D. thesis], Department of Computer Science and Engineering, Chalmers University Of Technology, 2006.
  46. W. Gropp, E. Lusk, and A. Skjellum, Using MPI: Portable Programming with the Message-Passing Interface, Massachusetts Institute of Technology press, 2nd edition, 1999.
  47. D. B. Kirk and W. W. Hwu, Programming Massively Parallel Processors: A Hands on Approach, Morgan Kaufmann, 2010.
  48. Nvidia Corporation, NVIDIA GeForce GTX 200 GPU Architectural Overview: Second-Generation Unified GPU Architecture for Visual Computing, 2008.
  49. NVIDIA Corporation, NVIDIA CUDA Programming Guide, version 3.0 edition, 2010.
  50. H. Chafi, A. K. Sujeeth, K. J. Brown, H. J. Lee, A. R. Atreya, and K. Olukotun, “A domain-specific approach to heterogeneous parallelism,” in Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming (PPoPP '11), pp. 35–46, ACM, New York, NY, USA, 2011.
  51. F. Bouchez, “Research report: GPGPU and Matlab,” Tech. Rep., Indian Institute of science, Bangalore, India, 2010. View at Google Scholar
  52. Accelereyes. Jacket 2. 0 documentation, 2011.
  53. A. Klockner, N. Pinto, Y. Lee, B. Catanzaro, P. Ivanov, and A. Fasih, “Pycuda and pyopencl: a scripting-based approach to gpu run-time code generation,” Parallel Computing, vol. 911, pp. 1–24, 2011. View at Google Scholar
  54. L. Madec, L. Falk, and E. Plasari, “Modelling of the agglomeration in suspension process with multidimensional kernels,” Powder Technology, vol. 130, no. 1–3, pp. 147–153, 2003. View at Publisher · View at Google Scholar · View at Scopus
  55. C. D. Immanuel and F. J. Doyle III, “Computationally efficient solution of population balance models incorporating nucleation, growth and coagulation: application to emulsion polymerization,” Chemical Engineering Science, vol. 58, no. 16, pp. 3681–3698, 2003. View at Publisher · View at Google Scholar · View at Scopus
  56. B. Shankar, L. Roh, W. Bhm, and W. Najjar, Control of Loop Parallelism in Multithreaded Code, 1995.
  57. R. Refianti, R. Refianti, and D. T. Hasta, “Workshare process of thread programming and mpimodel on multicore architecture,” International Journal of Advanced Computer Science and Applications, vol. 2, pp. 99–107, 2011. View at Google Scholar
  58. M. D. Haines, Distributed runtime support for task nd data management [Ph.D. thesis], Colorado State University, 1993.
  59. Y. Zhang, F. Mueller, X. Cui, and T. Potok, “Data-intensive document clustering on graphics processing unit (GPU) clusters,” Journal of Parallel and Distributed Computing, vol. 71, no. 2, pp. 211–224, 2011. View at Publisher · View at Google Scholar · View at Scopus
  60. M. M. K. Martin, M. D. Hill, and D. J. Sorin, “Why on-chip cache coherence is here to stay,” Tech. Rep., Department of ECE, Duke University, 2011. View at Google Scholar
  61. R. Brightwell, W. Camp, B. Cole et al., “Architectural specification for massively parallel computers: an experience and measurement-based approach,” Concurrency Computation Practice and Experience, vol. 17, no. 10, pp. 1271–1316, 2005. View at Publisher · View at Google Scholar · View at Scopus
  62. S. G. Akl, “Superlinear performance in real-time parallel computation,” Journal of Supercomputing, vol. 29, no. 1, pp. 89–111, 2004. View at Publisher · View at Google Scholar · View at Scopus
  63. J. L. Gustafson, G. R. Montry, R. E. Benner, and C. W. Gear, “Development of parallel methods for a 1024-processor hypercube,” SIAM Journal on Scientific and Statistical Computing, vol. 9, pp. 609–638, 1988. View at Google Scholar
  64. S. D. Schaber, D. I. Gerogiorgis, R. Ramachandran, J. M. B. Evans, P. I. Barton, and B. L. Trout, “Economic analysis of integrated continuous and batch pharmaceutical manufacturing: a case study,” Industrial and Engineering Chemistry Research, vol. 50, pp. 10083–10092, 2011. View at Google Scholar