Gradient Coding

Improving mapreduce performance in heterogeneous environments

Zaharia, M., Konwinski, A., Joseph, A. D., Katz, R. H., and Stoica, I. (2008) · 2008

Earlier work this paper cites.

Parallel distributed computing using python

Dalcin, L. D., Paz, R. R., Kler, P. A., and Cosimo, A. (2011) · 2011

Earlier work this paper cites.

Large scale distributed deep networks

Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P., Yang, K., Le, Q. V., and Ng, A. Y. (2012) · 2012

Earlier work this paper cites.

Balanced sparsest generator matrices for mds codes

Dau, S. H., Song, W., Dong, Z., and Yuen, C. (2013) · 2013

Earlier work this paper cites.

More effective distributed ml via a stale synchronous parallel parameter server

Ho, Q., Cipar, J., Cui, H., Lee, S., Kim, J. K., Gibbons, P. B., Gibson, G. A., Ganger, G., and Xing, E. P. (2013) · 2013

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Towards resource-elastic machine learning

Narayanamurthy, S., Weimer, M., Mahajan, D., Condie, T., Sellamanickam, S., and Keerthi, S. S. (2013) · 2013

Cited alongside, same era.

First-order methods of smooth convex optimization with inexact oracle

Devolder, O., Glineur, F., and Nesterov, Y. (2014) · 2014

Cited alongside, same era.

Communication efficient distributed machine learning with the parameter server

Li, M., Andersen, D. G., Smola, A. J., and Yu, K. (2014) · 2014

Cited alongside, same era.

Speeding up distributed machine learning using codes

Original

Lee, K., Lam, M., Pedarsani, R., Papailiopoulos, D. S., and Ramchandran, K. (2015) · 2015

Cited alongside, same era.

A unified coding framework for distributed computing with straggling servers

Original

Li, S., Maddah-Ali, M. A., and Avestimehr, A. S. (2016a)

Cited in the paper.

A fundamental tradeoff between computation and communication in distributed computing

Original

Li, S., Maddah-Ali, M. A., Yu, Q., and Avestimehr, A. S. (2016b)

Cited in the paper.

Coded mapreduce

Li, S., Maddah-Ali, M. A., and Avestimehr, A. S. (2015) · 2015

Later among the works it cites.

Revisiting Distributed Synchronous SGD

Chen, J., Monga, R., Bengio, S., and Jozefowicz, R. (2016) · 2016

Closest in time.

Short-dot: Computing large linear transforms distributedly using coded short dot products

Dutta, S., Cadambe, V., and Grover, P. (2016) · 2016

Closest in time.

Asynchrony begets momentum, with an application to deep learning

Original

Mitliagkas, I., Zhang, C., Hadjis, S., and Ré, C. (2016) · 2016

Closest in time.