Distributed Cache Strategies for Machine Learning Classification Tasks over Cluster Computing Resources - High Performance Computing - page 52

goes to data” as observed in other Big Data related contexts, and allows us to
confidently envision strategies for parallelizing ML algorithms and aligning the
design of computing infrastructures to solving specific ML problems.
Acknowledgements. This work was partially funded by projects “Multimodal
Image Retrieval to Support Medical Case-Based Scientific Literature Search”,
ID R1212LAC006 by Microsoft Research LACCIR, “Diseño e implementación
de un sistema de cómputo sobre recursos heterogéneos para la identificación de
estructuras atmosféricas en predicción climatológica” number 1225-569-34920
through Colciencias contract number 0213-2013 and “Proyecto Centro de Super-
computación de la Universidad Nacional de Colombia”. John Arevalo also thanks
Colciencias for its support through a doctoral grant in call 617 2013. Authors
also thank the support of the High Performance and Scientific Computing Centre
and Universidad Industrial de Santander ( http://sc3.uis.edu.co ) .
References
1. Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Pro-
ceedings of COMPSTAT 2010, pp. 177-186. Springer (2010)
2. Chu, C., Kim, S.K., Lin, Y.-A., Yu, Y., Bradski, G., Ng, A.Y., Olukotun, K.:
Map-reduce for machine learning on multicore. Advances in neural information
processing systems 19, 281 (2007)
3. Coates, A., Huval, B., Wang, T., Wu, D., Catanzaro, B., Andrew, N.: Deep learning
with cots hpc systems. In: Proceedings of the 30th International Conference on
Machine Learning, pp. 1337-1345 (2013)
4. Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., Senior, A.,
Tucker, P., Yang, K., Le, Q.V., et al.: Large scale distributed deep networks. In:
Advances in Neural Information Processing Systems, pp. 1223-1231 (2012)
5. Hsu, D., Karampatziakis, N., Langford, J., Smola, A.J.: Parallel online learning.
CoRR, abs/1103.4204 (2011)
6. Kraska, T., Talwalkar, A., Duchi, J.C., Gri th, R., Franklin, M.J., Jordan, M.I.:
Mlbase: A distributed machine-learning system. In: CIDR (2013)
7. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to
document recognition. Proceedings of the IEEE 86(11), 2278-2324 (1998)
8. Navruzyan, A.: Online machine learning with distributed in-memory clusters (2013)
9. Ramos-Pollan, R., Cruz-Roa, A., Gonzalez, F.A.: A framework for high perfor-
mance image analysis pipelines. In: 2012 7th Colombian Computing Congress
(CCC), pp. 1-6 (October 2012)
10. Ramos-Pollan, R., Gonzalez, F.A., Caicedo, J.C., Cruz-Roa, A., Camargo, J.E.,
Vanegas, J.A., Perez, S.A., Bermeo, J.D., Otalora, J.S., Rozo, P.K., Arevalo, J.:
Bigs: A framework for large-scale image processing and analysis over distributed
and heterogeneous computing resources. In: 2012 IEEE 8th International Confer-
ence on E-Science (e-Science), pp. 1-8 (October 2012)
11. Rosen, J., Polyzotis, N., Borkar, V., Bu, Y., Carey, M.J., Weimer, M., Condie, T.,
Ramakrishnan, R.: Iterative mapreduce for large scale machine learning. arXiv
preprint arXiv:1303.3517 (2013)