Optimization of power consumption in the iterative solution of sparse linear systems on graphics processors-Reference-Cited by-同舟云学术

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Optimization of power consumption in the iterative solution of sparse linear systems on graphics processors

Published:2011-08-31 Issue:4 Volume:27 Page:299-307
ISSN:1865-2034
Container-title:Computer Science - Research and Development
language:en
Short-container-title:Comput Sci Res Dev

Author:

Anzt Hartwig,Castillo Maribel,Fernández Juan C.,Heuveline Vincent,Igual Francisco D.,Mayo Rafael,Quintana-Ortí Enrique S.

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

http://link.springer.com/content/pdf/10.1007/s00450-011-0195-8.pdf

Reference13 articles.

1. Lampe F (2010) Green-IT, Virtualisierung und Thin Clients: Mit neuen IT-Technologien Energieeffizienz erreichen, die Umwelt schonen und Kosten sparen. Vieweg/Teubner, Wiesbaten

2. Kogge P et al (2008) ExaScale computing study: technology challenges in achieving ExaScale systems

3. Dongarra J et al (2011) The international ExaScale software project roadmap. doi: 10.1177/1094342010391989

4. Saad Y (2003) Iterative methods for sparse linear systems. Society for Industrial and Applied Mathematics, Philadelphia

5. Saad Y, Schultz MH (1986) Gmres: a generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM J Sci Stat Comput 7:856–869. Available: http://portal.acm.org/citation.cfm?id=14063.14074

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1. Leakage/Temperature-Aware Dynamic Voltage Scaling and Dynamic Cache Reconfiguration to Reduce Power Consumption;Journal of Low Power Electronics;2018-06-01

2. A survey of power and energy efficient techniques for high performance numerical linear algebra operations;Parallel Computing;2014-12

3. Improving the energy efficiency of sparse linear system solvers on multicore and manycore systems;Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences;2014-06-28

4. Parallel resolution of the 3D Helmholtz equation based on multi-graphics processing unit clusters;Concurrency and Computation: Practice and Experience;2014-02-05

5. Reformulated Conjugate Gradient for the Energy-Aware Solution of Linear Systems on GPUs;2013 42nd International Conference on Parallel Processing;2013-10

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