SparkNet: Training Deep Networks in Spark

Dryad: Distributed data-parallel programs from sequential building blocks

Isard, Michael, Budiu, Mihai, Yu, Yuan, Birrell, Andrew, and Fetterly, Dennis · 2007

Earlier work this paper cites.

MapReduce: simplified data processing on large clusters

Dean, Jeffrey and Ghemawat, Sanjay · 2008

Earlier work this paper cites.

Spark: cluster computing with working sets

Zaharia, Matei, Chowdhury, Mosharaf, Franklin, Michael J, Shenker, Scott, and Stoica, Ion · 2010

Earlier work this paper cites.

Parallelized stochastic gradient descent

Zinkevich, Martin, Weimer, Markus, Li, Lihong, and Smola, Alex J · 2010

Earlier work this paper cites.

Large scale distributed deep networks

Dean, Jeffrey, Corrado, Greg, Monga, Rajat, Chen, Kai, Devin, Matthieu, Mao, Mark, Ranzato, Marc’Aurelio, Senior, Andrew, Tucker, Paul, Yang, Ke, Le, Quoc V., and Ng, Andrew Y · 2012

Earlier work this paper cites.

Imagenet classification with deep convolutional neural networks

Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012

Earlier work this paper cites.

Deep learning with cots hpc systems

Coates, Adam, Huval, Brody, Wang, Tao, Wu, David, Catanzaro, Bryan, and Andrew, Ng · 2013

Earlier work this paper cites.

More effective distributed ML via a stale synchronous parallel parameter server

Ho, Qirong, Cipar, James, Cui, Henggang, Lee, Seunghak, Kim, Jin Kyu, Gibbons, Phillip B, Gibson, Garth A, Ganger, Greg, and Xing, Eric P · 2013

Earlier work this paper cites.

Naiad: a timely dataflow system

Murray, Derek G, McSherry, Frank, Isaacs, Rebecca, Isard, Michael, Barham, Paul, and Abadi, Martín · 2013

Earlier work this paper cites.

Discretized streams: Fault-tolerant streaming computation at scale

Zaharia, Matei, Das, Tathagata, Li, Haoyuan, Hunter, Timothy, Shenker, Scott, and Stoica, Ion · 2013

Cited alongside, same era.

Project Adam: Building an efficient and scalable deep learning training system

Chilimbi, Trishul, Suzue, Yutaka, Apacible, Johnson, and Kalyanaraman, Karthik · 2014

Cited alongside, same era.

Graphx: Graph processing in a distributed dataflow framework

Gonzalez, Joseph E, Xin, Reynold S, Dave, Ankur, Crankshaw, Daniel, Franklin, Michael J, and Stoica, Ion · 2014

Cited alongside, same era.

Caffe: Convolutional architecture for fast feature embedding

Jia, Yangqing, Shelhamer, Evan, Donahue, Jeff, Karayev, Sergey, Long, Jonathan, Girshick, Ross, Guadarrama, Sergio, and Darrell, Trevor · 2014

Cited alongside, same era.

Scaling distributed machine learning with the parameter server

Li, Mu, Andersen, David G, Park, Jun Woo, Smola, Alexander J, Ahmed, Amr, Josifovski, Vanja, Long, James, Shekita, Eugene J, and Su, Bor-Yiing · 2014

Cited alongside, same era.

FireCaffe: near-linear acceleration of deep neural network training on compute clusters

Original

Iandola, Forrest N, Ashraf, Khalid, Moskewicz, Mattthew W, and Keutzer, Kurt · 2015

Closest in time.

MLlib: Machine learning in Apache Spark

Original

Meng, Xiangrui, Bradley, Joseph, Yavuz, Burak, Sparks, Evan, Venkataraman, Shivaram, Liu, Davies, Freeman, Jeremy, Tsai, DB, Amde, Manish, Owen, Sean, et al · 2015

Closest in time.

Large scale distributed deep learning on Hadoop clusters, 2015

Noel, Cyprien, Shi, Jun, and Feng, Andy · 2015

Closest in time.

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael, Berg, Alexander C., and Fei-Fei, Li · 2015

Closest in time.

KeystoneML: End-to-end machine learning pipelines at scale

Sparks, Evan R., Venkataraman, Shivaram, Kaftan, Tomer, Franklin, Michael, and Recht, Benjamin · 2015