• Norma Alias Center for Sustainable Nanomaterials (CSNano), Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Masyitah Mohd Saidi Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia




Ice thickness, 2-D ice flow, Finite Difference Method, Explicit Method, CUDA


Two-dimensional (2-D) ice flow thermodynamics coupled model acts as a vital role for visualizing the ice sheet behaviours of the Antarctica region and the climate system. One of the parameters used in this model is ice thickness. Explicit method of finite difference method (FDM) is used to discretize the ice thickness equation. After that, the equation will be performed on Compute Unified Device Architecture (CUDA) programming by using Graphics Processing Unit (GPU) platform. Nowadays, the demand of GPU for solving the computational problem has been increasing due to the low price and high performance computation properties. This paper investigates the performance of GPU hardware supported by the CUDA parallel programming and capable to compute a large sparse complex system of the ice thickness equation of 2D ice flow thermodynamics model using multiple cores simultaneously and efficiently. The parallel performance evaluation (PPE) is evaluated in terms of execution time, speedup, efficiency, effectiveness and temporal performance.


Huybrechts, P., Gregory, J., Janssens, I. and Wild, M. 2004. Modelling Antarctic and Greenland Volume Changes During The 20th And 21st Centuries Forced By GCM Time Slice Integrations. Global and Planetary Change. 42: 83-105.

Huybrechts, P., Steinhage, D., Wilhelms, F., Bamber, J. L. 2000. Balance Velocities And Measured Properties Of The Antarctic Ice Sheet From A New Compilation Of Gridded Datasets For Modeling. Annals of Glaciology. 30: 52–60.

IPCC-AR5, Fifth Assessment Report: Climate Change 2013: The Physical Science Basis. IPCC, 2013.

Zhang, K., Lu, J., Lafruit, G., Lauwereins, R. and Gool, L. V. 2011. Real-Time And Accurate Stereo: A Scalable Approach With Bitwise Fast Voting On CUDA. IEEE Transactions on Circuits and Systems for Video Technology. 2(7): 867-878.

Alias, N., et al., 2011. Performance Evaluation Of Multidimensional Parabolic Type Problems On Distributed Computing Systems. 2011 IEEE Symposium on Computers and Communications (ISCC).

Alias, N. and M. R. Islam, 2010. A Review Of The Parallel Algorithms For Solving Multidimensional PDE Problems. Journal of Applied Sciences. 10(19): 2187-2197.

Alias, N., et al., 2009. Parallelization of Temperature Distribution Simulations for Semiconductor and Polymer Composite Material on Distributed Memory Architecture. Parallel Computing Technologies. Proceedings. 5698: 392-398.

Brædstrup, C. F., Damsgaard, A. and Egholm, D. L. 2014. Ice - Sheet Modelling Accelerated By Graphics Cards. Computers & Geosciences. 72: 210–220.

Robin, G. de Q. 1955. Ice Movement And Temperature Distribution In Glaciers And Ice Sheets. J. GIaciology. 2: 523-532.

Budd, W. F. Jenssen, D. and Radok, U. 1971. Derived Physical Characteristics Of The Antarctic Ice Sheet, ANARE Interim Report. Glaciology Publication. 120: 178.

Budd, W. F. and Jenssen, D. 1975. Numerical Modelling Of Glacier Systems. IAHS Publication. 104: 257-291.

Mahaffy, M. W. 1976. A Three-Dimensional Nurnerical Rnodel Of Ice Sheets: Tests On The Barnes Ice Cap, Northwest Territories, J. Geophvs. Res. 81(6): 1059-1066.

Hutter, K., Yakowitz, S. and Szidarovszky, F. 1986. A Nurnerical Study Of Plane Ice-Sheet Flow. IJ. GIaciol. 32(1111): 139-160.

Hindmarsh, R. C. A., Boulton, G. S. and Hutter, K. 1989. Modes Of Operation Of Thermo-Mechanically Coupled Ice Sheets. Ann. Glaciol. 12: 57-69.

Oerlernans, J. 1982a. Response of the Antarctic ice sheet To A Clirnatic Warrning: A Rnodel Study. J.CIimat. 2: 1-11.

Oerlernans, J. 1982b. A Rnodel Of The Antarctic Ice Sheet. Nature. 297: 550-553.

Payne A. J. 1999. A Thermomechanical Model Of Ice Flow in West Antarctica. Climate Dynamics. 15: 115–25.

Bueler, E., Brown, J. and Lingle, C. 2007. Exact Solutions To The Thermomechanically Coupled Shallow-Ice Approximation: Effective Tools For Verification. Journal of Glaciology. 53: 499-516.

Jamieson, S. S. R., Hulton, N. R. J. and Hagdorn, M. 2008. Modelling Landscape Evolution Under Ice Sheets. Geomorphology. 97: 91-108.

NVIDIA, 2009a. CUDA Architecture, Introduction & Overview, Version 1.1.

Tutkun, B. and Edis, F.O. 2012. A GPU Application For High-Order Compact Finite Difference Scheme. Computers & Fluids. 55: 29-35.

Gravvanis, G. A, Filelis-Papadopoulos, C. K. and Giannoutakis, K. M. 2012. Solving Finite Difference Linear Systems On Gpus: Cuda Based Parallel Explicit Preconditioned Biconjugate Conjugate Gradient Type Methods. Journal Supercomputing. 61: 590-604.

Tang, X., Zhang, Z., Sun, B., Li, Y., Li, N., Wang, B. and Zhang, X. 2008. Antarctic Ice Sheet GLIMMER Model Test And Its Simplified Model On 2-Dimensional Ice Flow. Progress in Natural Science. 18: 173–180.

Alias, N., Kasmin, B. N. K. and Mahmud, N. A. 2014. Some Numerical Methods for Ice Sheet Behaviour and Its Visualization. J. Appl. Environ. Biol. Sci. 4(8S): 351-357.

Van Der Veen, C. J. 1999. Fundamentals Of Glacier Dynamics. Balkeema.

Huybrechts, P. and et. al. 1996. The EISMINT Benchmarks For Testing Ice–Sheet Models. Annals Glaciology. 23: 1–12.

Sanders, J., Kandrot, E. 2010. CUDA by Example: An Introduction to General-Purpose GPU Programming, 1st Edition Addison-Wesley Professional, Boston, USA.

Cheng, J., Grossman, M. and McKercher, T. 2014. Professional CUDA C Programming. John Willey & Sons, Inc.




How to Cite

HIGH SPEED COMPUTING OF ICE THICKNESS EQUATION FOR ICE SHEET MODEL. (2016). Jurnal Teknologi, 78(8-2). https://doi.org/10.11113/jt.v78.9551