Incremental, iterative data processing with timely dataflow
By Derek Gordon Murray, Frank McSherry, Michael Isard, Rebecca Isaacs, Paul Barham, Martín Abadi. 2016
51 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Hardware and Architecture, Information Systems, ML Systems & Hardware
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Yi Yang
- Dacheng Tao
- Andrew Zisserman
- Xiaogang Wang
- Ming-Hsuan Yang
- Jitendra Malik
- Quoc V. Le
In Hardware and Architecture
- Derek Gordon Murray
- Michael Isard
- Martín Abadi
- Paul Barham
- Sanjay Ghemawat
- M. Burrows
- Andy Davis
- Yuan Yu
In Information Systems
- Michael Isard
- Derek Gordon Murray
- Rajat Monga
- Martín Abadi
- Paul Barham
- Sanjay Ghemawat
- M. Burrows
- Joseph Dean
In ML Systems & Hardware
- Derek Gordon Murray
- Michael Isard
- Martín Abadi
- Rajat Monga
- Paul Barham
- Sanjay Ghemawat
- M. Burrows
- Joseph Dean
Citations in the corpus
- A computational model for TensorFlow: an introduction (2017, methodology): “In our past work on timely dataflow [16], we found that having a semantics [3] was crucial for the design of correct techniques for fault-tolerance [4]; a semantics could play an analogous role in future work on Tensor-Flow.”
- Dynamic control flow in large-scale machine learning (2018, methodology): “Also, continuous training and inference may rely on streaming systems, perhaps using the dynamic dataflow architectures that we adapt, as suggested by work on timely dataflow and differential dataflow [28].”