Abstract
We investigate the effect that commonoptimization techniques for general-purpose multicore processors (either manual, compiler-driven, in the form of highly tuned libraries, or orchestrated by a runtime) exert on the performance-power-energy trade-off of dense linear algebra routines. The algorithm employed for this analysis is matrix inversion via Gauss-Jordan elimination, but the results from the evaluation carry beyond this particular operation and are representative for a variety of dense linear algebra computations, especially, dense matrix factorizations.
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Benner, P., Ezzatti, P., Quintana-Ortí, E., Remón, A. (2013). On the Impact of Optimization on the Time-Power-Energy Balance of Dense Linear Algebra Factorizations. In: Aversa, R., Kołodziej, J., Zhang, J., Amato, F., Fortino, G. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2013. Lecture Notes in Computer Science, vol 8286. Springer, Cham. https://doi.org/10.1007/978-3-319-03889-6_1
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DOI: https://doi.org/10.1007/978-3-319-03889-6_1
Publisher Name: Springer, Cham
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