Abstract
The field of high-performance computing is highly dependent on increasingly complex computer architectures. Parallel computing has been the norm for decades, but hardware architectures like the Cell Broadband Engine (Cell/BE) and General Purpose GPUs (GPGPUs) introduce additional complexities and are difficult to program efficiently even for well-suited problems. Efficiency is taken to include both maximizing the performance of the software and minimizing the programming effort required. With the goal of exposing the challenges facing a domain scientist using these types of hardware, in this paper we discuss the implementation of a Monte Carlo simulation of a system of charged particles on the Cell/BE and for GPUs. We focus on Coulomb interactions because their long-range nature prohibits using cut-offs to reduce the number of calculations, making simulations very expensive. The goal was to encapsulate the computationally expensive component of the program in a way so as to be useful to domain researchers with legacy codes. Generality and flexibility were therefore just as important as performance. Using the GPU and Cell/BE library requires only small changes in the simulation codes we’ve seen and yields programs that run at or near the theoretical peak performance of the hardware.
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References
Arevalo, A., Matinata, R.M., Pandian, M., Peri, E., Ruby, K., Thomas, F., Almond, C.: Programming for the Cell Broadband Engine. IBM Redbooks (2008)
Datta, K., Murphy, M., Volkov, V., Williams, S., Carter, J., Oliker, L., Patterson, D., Shalf, J., Yelick, K.: Stencil Computation Optimization and Auto-tuning on State-of-the-Art Multicore Architectures. In: International Conference for High Performance Computing, Networking, Storage and Analysis (2008)
Davidson, A., Owens, J.D.: Toward Techniques for Auto-tuning GPU Algorithms. In: Jónasson, K. (ed.) PARA 2010, Part II. LNCS, vol. 7134, pp. 110–119. Springer, Heidelberg (2012)
Farber, R.: Cuda, supercomputing for the masses. Dr Dobbs (2008)
Khan, M.O.: Polymer Electrostatics: From DNA to Polyampholytes. PhD thesis, Lund University (2001)
Khan, M.O., Kennedy, G., Chan, D.Y.C.: A scalable parallel monte carlo method for free energy simulations of molecular systems. Journal of Computational Chemistry 26(1), 72–77 (2005)
Nyland, L., Harris, M., Prins, J.: GPU Gems 3. In: Fast N-Body Simulation with CUDA, ch.31. Pearson Education, Inc. (2007)
Williams, S., Carter, J., Oliker, L., Shalf, J., Yelick, K.: Lattice Boltzmann simulation optimization on leading multicore platforms. In: IEEE International Symposium on Parallel and Distributed Processing, Vols. 1-8 (2008)
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Holm, M., Holmgren, S. (2012). Efficiently Implementing Monte Carlo Electrostatics Simulations on Multicore Accelerators. In: Jónasson, K. (eds) Applied Parallel and Scientific Computing. PARA 2010. Lecture Notes in Computer Science, vol 7134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28145-7_37
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DOI: https://doi.org/10.1007/978-3-642-28145-7_37
Publisher Name: Springer, Berlin, Heidelberg
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