Energy-efficient execution of dense linear algebra algorithms on multi-core processors-Reference-Cited by-同舟云学术

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Energy-efficient execution of dense linear algebra algorithms on multi-core processors

Published:2012-05-12 Issue:3 Volume:16 Page:497-509
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Alonso Pedro,Dolz Manuel F.,Mayo Rafael,Quintana-Ortí Enrique S.

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

http://link.springer.com/content/pdf/10.1007/s10586-012-0215-x.pdf

Reference33 articles.

1. Borkar, S., Chien, A.: The future of microprocessors. Commun. ACM 54, 67–77 (2011)

2. Esmaeilzadeh, H., Blem, E., Amant, R.St., Sankaralingam, K., Burger, D.: Dark silicon and the end of multicore scaling. In: Proceeding of the 38th Annual International Symposium on Computer Architecture, ISCA’11, New York, NY, USA, pp. 365–376. ACM Press, New York (2011)

3. Dongarra, J., Beckman, P., Moore, T., Aerts, P., Aloisio, G., Andre, J.C., Barkai, D., Berthou, J.Y., Boku, T., Braunschweig, B., et al.: The international exascale software project roadmap. Int. J. High Perform. Comput. Appl. 25(1), 3 (2011)

4. Duranton, M., et al.: The HiPEAC vision (2010). Available from http://www.hipeac.net/roadmap

5. Feng, W.-c., Feng, X., Ce, R.: Green supercomputing comes of age. IT Prof. 10(1), 17–23 (2008)

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2. ALEA: Fine-Grain Energy Profiling with Basic Block Sampling;2015 International Conference on Parallel Architecture and Compilation (PACT);2015-10

3. Experiences in autotuning matrix multiplication for energy minimization on GPUs;Concurrency and Computation: Practice and Experience;2015-05-20

4. Assessing the impact of the CPU power-saving modes on the task-parallel solution of sparse linear systems;Cluster Computing;2014-09-05

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