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Presented by: Miguel Cabral Advised by: Dr. Fan Wu

Numerical Solutions of Heat and Mass Transfer in Capillary Porous Media Using Programmable Graphics Hardware. Presented by: Miguel Cabral Advised by: Dr. Fan Wu. Outline. Introduction Previous Work Current Work Experimental Results Future Work. Introduction.

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Presented by: Miguel Cabral Advised by: Dr. Fan Wu

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  1. Numerical Solutions of Heat and Mass Transfer in Capillary Porous Media Using Programmable Graphics Hardware Presented by: Miguel Cabral Advised by: Dr. Fan Wu

  2. Outline Introduction Previous Work Current Work Experimental Results Future Work

  3. Introduction • Many scientists and engineers have focused on solutions both numerically and experimentally coupled to Heat and Mass Transfer problems recently. • Applications • Absorption of nutrients in human bodies • Transpiration cooling of space vehicles at re-entry into atmosphere, • Contaminants Transport in the ground water • Simulation of heat and mass transfer requires long execution time

  4. Previous Work • Fast matrix multiplication using GPUs • This work has been published in the proceeding of the 2010 International Conference on Computational Intelligence and Software Engineering (CiSE 2010) • Hira Narang, Fan Wu, and Miguel Cabral, ??? High Performance Matrix Multiplication on General Purpose Graphics Processing Units • Numerical solutions to some heat and mass transfer equations • This work has been published in the proceeding of the 2011 World Congress On Computer Science and Information Engineering. • Hira Narang, Fan Wu, and Miguel Cabral, Numerical Solutions of Heat and Mass Transfer in Capillary Porous Media Using Programmable Graphics Hardware

  5. Previous Experimental Results • Our experiments were carried out in • 64-bit Lenovo Thinkstation D20 with an Intel Xeon CPU E5520 @ 2.27 GHZ • NVIDIA Quadro FX 4800 • The error of Concentration values between CPU and GPU results were minimal even for large values of N • N < 28, the CPU performs faster than the GPU. • 28 < N < 210 , CPU and GPU perform the same. • N > 210, GPU performs begin to increase considerably • N is the number of sample points

  6. Current Work • My goal was to find a better solution to optimize my previous implementation of a heat equation solver • I explored new memory types to optimize data access • Texture memory • It is a type of read-only memory that can improve performance and reduce memory traffic when reads have certain access patterns • It was originally designed for graphics applications where memory access patterns exhibit a great deal of spatial locality • The solver was faster, but not fast enough • The solution was implemented in CUDA using 2 different types of memory (global vs. texture)

  7. Experimental Results (1/2) • The results show that there was a minimum improvement of performance as compared to our previous work. • GPU1 vs GPU2 GPU vs CPU

  8. Experimental Results (2/2) • The execution time was slightly better every time step but the improvement was not considerable

  9. Future Work • Our future work will focus on numerical solutions of heat and mass transfer equations in circular and cylindrical areas

  10. Questions?

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