Coded Computing for Distributed Graph Analytics

Graduate Texts in Mathematics, Springer New York, 1977

M. Loeve, Probability Theory I · 1977

Earlier work this paper cites.

E. Dahlhaus, D. S. Johnson, C. H. Papadimitriou, P. D. Seymour, and M. Yannakakis, “The complexity of multiway cuts,” STOC

1992

Earlier work this paper cites.

R. P. E., “Sen, p. k.; singer, j. m.: Large sample methods in statistics. an introduction with applications. chapman & hall, new york-london 1993, xii, 382pp., £35.00, isbn 0–412–04221–5,” Biometrical Journal

1993

Earlier work this paper cites.

L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bringing order to the web.,” Tech. Rep. 1999-66, Stanford InfoLab, 1999

1999

Earlier work this paper cites.

W. Xing and A. Ghorbani, “Weighted pagerank algorithm,” in Communication Networks and Services Research, 2004. Proceedings. Second Annual Conference on

2004

Earlier work this paper cites.

F. Chung and L. Lu, “The average distance in a random graph with given expected degrees,” Internet Mathematics

2004

Earlier work this paper cites.

M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly, “Dryad: distributed data-parallel programs from sequential building blocks,” EuroSys

2007

Earlier work this paper cites.

A. Lumsdaine, D. Gregor, B. Hendrickson, and J. Berry, “Challenges in parallel graph processing,” Parallel Processing Letters

2007

Earlier work this paper cites.

J. Dean and S. Ghemawat, “Mapreduce: simplified data processing on large clusters,” Communications of the ACM

2008

Earlier work this paper cites.

G. Malewicz, M. H. Austern, A. J. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski, “Pregel: a system for large-scale graph processing,” SIGMOD

2010

Earlier work this paper cites.

J. Lin and M. Schatz, “Design patterns for efficient graph algorithms in mapreduce,” MLG Workshop

2010

Earlier work this paper cites.

M. Fire, L. Tenenboim, O. Lesser, R. Puzis, L. Rokach, and Y. Elovici, “Link prediction in social networks using computationally efficient topological features,” in IEEE Third International Confernece on Social Computing (SocialCom)

2011

Earlier work this paper cites.

J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin, “Powergraph: distributed graph-parallel computation on natural graphs.,” in OSDI

2012

Earlier work this paper cites.

Y. Low, D. Bickson, J. Gonzalez, C. Guestrin, A. Kyrola, and J. M. Hellerstein, “Distributed graphlab: a framework for machine learning and data mining in the cloud,” VLDB

2012

Earlier work this paper cites.

Z. Khayyat, K. Awara, A. Alonazi, H. Jamjoom, D. Williams, and P. Kalnis, “Mizan: a system for dynamic load balancing in large-scale graph processing,” in Proceedings of the 8th ACM European Conference on Computer Systems

2013

Earlier work this paper cites.

R. Chen, X. Ding, P. Wang, H. Chen, B. Zang, and H. Guan, “Computation and communication efficient graph processing with distributed immutable view,” HPDC

2014

Earlier work this paper cites.

R. R. McCune, T. Weninger, and G. Madey, “Thinking like a vertex: a survey of vertex-centric frameworks for large-scale distributed graph processing,” ACM Computing Surveys

2015

Earlier work this paper cites.

S. Li, M. A. Maddah-Ali, and A. S. Avestimehr, “Coded distributed computing: Straggling servers and multistage dataflows,” in Communication, Control, and Computing (Allerton), 2016 54th Annual Allerton Conference on

2016

Cited alongside, same era.

M. A. Attia and R. Tandon, “Information theoretic limits of data shuffling for distributed learning,” GLOBECOM

2016

Cited alongside, same era.

S. Dutta, V. Cadambe, and P. Grover, “Short-dot: Computing large linear transforms distributedly using coded short dot products,” NIPS

2016

Cited alongside, same era.

S. Li, M. A. Maddah-Ali, and A. S. Avestimehr, “A unified coding framework for distributed computing with straggling servers,” in Globecom Workshops (GC Wkshps), 2016 IEEE

2016

Cited alongside, same era.

Y. Yang, P. Grover, and S. Kar, “Fault-tolerant distributed logistic regression using unreliable components,” in Communication, Control, and Computing (Allerton), 2016 54th Annual Allerton Conference on

Z. Charles, D. Papailiopoulos, and J. Ellenberg, “Approximate gradient coding via sparse random graphs,” arXiv preprint arXiv:1711.06771

Original

2017

Later among the works it cites.

C. Karakus, Y. Sun, S. Diggavi, and W. Yin, “Straggler mitigation in distributed optimization through data encoding,” in Advances in Neural Information Processing Systems

2017

Later among the works it cites.

N. Ferdinand, B. Gharachorloo, and S. C. Draper, “Anytime exploitation of stragglers in synchronous stochastic gradient descent,” in Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on

2017

Later among the works it cites.

S. Prakash, A. Reisizadeh, R. Pedarsani, and S. Avestimehr, “Coded computing for distributed graph analytics,” ISIT

2018

Closest in time.

K. Konstantinidis and A. Ramamoorthy, “Leveraging coding techniques for speeding up distributed computing,” arXiv preprint arXiv:1802.03049