Introduction
Multiphysics simulations are at the core of modern Computer Aided Engineering (CAE) allowing the analysis of multiple, simultaneously acting physical phenomena. These simulations often rely on Finite Element Methods (FEM) and the solution of large linear systems which, in turn, end up in multiple calls of the costly Sparse Matrix-Vector Multiplication (SpM×V) kernel. The major—and mostly inherent—performance problem of the this kernel is its very low flop:byte ratio, meaning that the algorithm must retrieve a significant amount of data from the memory hierarchy in order to perform a useful operation.
The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement n° RI-261557.
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References
Goumas, G., Kourtis, K., Anastopoulos, N., Karakasis, V., Koziris, N.: Performance evaluation of the sparse matrix-vector multiplication on modern architectures. The Journal of Supercomputing 50(1), 36–77 (2009)
Kourtis, K., Karakasis, V., Goumas, G., Koziris, N.: CSX: An Extended Compression Format for SpMV on Shared Memory Systems. In: Proceedings of the 16th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP 2011), pp. 247–256. ACM, San Antonio (2011)
Lyly, M., Ruokolainen, J., Järvinen, E.: ELMER – a finite element solver for multiphysics. In: CSC Report on Scientific Computing (1999–2000)
Pinar, A., Heath, M.T.: Improving performance of sparse matrix-vector multiplication. In: Proceedings of the 1999 ACM/IEEE Conference on Supercomputing. ACM, Portland (1999)
Vuduc, R., Demmel, J.W., Yelick, K.A.: OSKI: A library of automatically tuned sparse matrix kernels. Journal of Physics: Conference Series 16(521) (2005)
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Karakasis, V., Goumas, G., Nikas, K., Koziris, N., Ruokolainen, J., Råback, P. (2013). Using State-of-the-Art Sparse Matrix Optimizations for Accelerating the Performance of Multiphysics Simulations. In: Manninen, P., Öster, P. (eds) Applied Parallel and Scientific Computing. PARA 2012. Lecture Notes in Computer Science, vol 7782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36803-5_40
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