Abstract
Scientific research is becoming increasingly dependent on the large-scale analysis of data using High Performance Computing (HPC) infrastructures. Scientific computing aims at constructing mathematical models and numerical solution techniques for solving problems arising in science and engineering. The solution of linear system of equations lies at the heart of most calculations in scientific computing. HPC infrastructures with many-core and graphics processing unit (GPU) challenges, Cloud and Grid technologies and e-infrastructures are currently offering interesting opportunities for solving large-scale linear system of equations. In this article, a second-generation of our Web portal for Scientific Computing is introduced based on a hybrid HPC infrastructure that provides predictable optimal execution and scales from a single resource to multiple resources. After analyzing the synergies and the complementarities of the different computing platforms, we argue for an architecture that combines the benefits of these technologies.
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References
Petitet, A., Casanova, H., Dongarra, J., Robert, Y., Whaley, R.: Parallel and Distributed Scientific Computing: A Numerical Linear Algebra Problem Solving Environment Designer’s Perspective. In: Handbook on Parallel and Distributed Processing (1999)
Owens, J., Houston, M., Luebke, D., Green, S., Stone, J., Phillips, J.: GPU computing. Proceedings of the IEEE 96(5), 879–899 (2008)
General-purpose computation using graphics hardware, http://www.gpgpu.org
MUMPS: a MUltifrontal Massively Parallel sparse direct Solver, http://mumps.enseeiht.fr
Blackford, L.S., Choi, J., Cleary, A., D’Azevedo, E., Demmel, J., Dhillon, I., Dongarra, J., Hammarling, S., Henry, G., Petitet, A., Stanley, K., Walker, D., Whaley, R.C.: ScaLAPACK: A Linear Algebra Library for Message-Passing Computers. In: SIAM Conference on Parallel Processing (1997)
Blackford, S., Demmel, J., Dongarra, J., Duff, I., Hammarling, S., Henry, G., Heroux, M., Kaufman, L., Lumsdaine, A., Petitet, A., Pozo, R., Remington, K.: An Updated Set of Basic Linear Algebra Subprograms. ACM Trans. Math. Soft. 28(2), 135–151 (2002)
Barrachina, S., Castillo, M., et al.: Evaluation and tuning of the Level 3 CUBLAS for graphics processors. In: IEEE International Symposium on Parallel and Distributed Processing, pp. 1–8. IEEE (April 2008)
Astsatryan, H., Sahakyan, V., Shoukouryan, Y., Daydé, M., Hurault, A., Pantel, P., Caron, E.: A Grid-Aware Web Interface with Advanced Service Trading for Linear Algebra Calculations. In: High Performance Computing for Computational Science - VECPAR 2008. LNCS, vol. 4395, Springer, Toulouse (2008)
Astsatryan, H., Sahakyan, V., Shoukourian, Y., Srapyan, M., Daydé, M., Hurault, A., Grigoras, R.: Introduction of a Grid-Aware Portlet for Numerical Calculations. In: IEEE 1st International Conference on Parallel, Distributed and Grid Computing (PDGC 2010), Waknaghat, Solan (HP), India (2010)
Astsatryan, H., Shoukouryan, Y., Sahakyan, V.: Grid Activities in Armenia. In: Proceedings of the International Conference Parallel Computing Technologies (PAVT 2009), Novgorod, Russia (March 30-April 3, 2009)
NVIDIA® TeslaTM C1060 computing processor enables, http://www.nvidia.com/docs/IO/43395/NV_DS_Tesla_C1060_US_Jan10_lores_r1.pdf
Németh, C., Dózsa, G., Lovas, R., Kacsuk, P.: The P-GRADE Grid Portal. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3044, pp. 10–19. Springer, Heidelberg (2004)
Duato, J., Antonio, J.P., Federico, S., Rafael, M., Enrique, S.: Quintana-Ortsíi, rCUDA: Reducing the number of GPU-based accelerators in high performance clusters. doi:10.1109/HPCS.2010.5547126
EGEE (Enabling Grids for E-sciencE) Project, http://glite.web.cern.ch/glite
Hurault, A., Daydé, M., Pantel, M.: Advanced service trading for scientific computing over the grid. Journal of Supercomputing, Springer Netherlands 49(1), 64–83 (2008)
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Astsatryan, H., Sahakyan, V., Shoukouryan, Y., Daydé, M., Hurault, A. (2012). Enabling Large-Scale Linear Systems of Equations on Hybrid HPC Infrastructures. In: Kocarev, L. (eds) ICT Innovations 2011. ICT Innovations 2011. Advances in Intelligent and Soft Computing, vol 150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28664-3_22
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DOI: https://doi.org/10.1007/978-3-642-28664-3_22
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