Fetching the paper…
Reading the bibliography…
Linear algebra operations are widely used in scientific computing and machine learning applications.
Leases: An efficient fault-tolerant mechanism for distributed file cache consistency
1989
Earlier work this paper cites.
Scalapack: A portable linear algebra library for distributed memory computers - design issues and performance
1996
Earlier work this paper cites.
Dryad: distributed data-parallel programs from sequential building blocks
2007
Earlier work this paper cites.
Numerical linear algebra on emerging architectures: The plasma and magma projects
2009
Earlier work this paper cites.
Communication-optimal parallel and sequential cholesky decomposition
2010
Earlier work this paper cites.
Hama: An efficient matrix computation with the mapreduce framework
2010
Earlier work this paper cites.
Communication-avoiding qr decomposition for gpus
2011
Earlier work this paper cites.
Minimizing communication in numerical linear algebra
2011
Earlier work this paper cites.
Flexible development of dense linear algebra algorithms on massively parallel architectures with dplasma
2011
Earlier work this paper cites.
Auto-scaling to minimize cost and meet application deadlines in cloud workflows
2011
Earlier work this paper cites.
Ciel: a universal execution engine for distributed data-flow computing
2011
Cited alongside, same era.
Efficient autoscaling in the cloud using predictive models for workload forecasting
2011
Cited alongside, same era.
A checkpoint-on-failure protocol for algorithm-based recovery in standard mpi
2012
Cited alongside, same era.
Dague: A generic distributed dag engine for high performance computing
2012
Cited alongside, same era.
Communication avoiding and overlapping for numerical linear algebra
2012
Cited alongside, same era.
Madlinq: large-scale distributed matrix computation for the cloud
2012
Cited alongside, same era.
Network requirements for resource disaggregation
2016
Later among the works it cites.
Proximal: Efficient image optimization using proximal algorithms
2016
Later among the works it cites.
Serverless computation with openlambda
2016
Later among the works it cites.
Mllib: Machine learning in apache spark
2016
Later among the works it cites.
Ernest: Efficient performance prediction for large-scale advanced analytics
2016
Later among the works it cites.
Encoding, fast and slow: Low-latency video processing using thousands of tiny threads
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Elemental: A new framework for distributed memory dense matrix computations
2013
Cited alongside, same era.
Proximal algorithms
2014
Cited alongside, same era.
Dask: Parallel computation with blocked algorithms and task scheduling
2015
Cited alongside, same era.
Systemml: Declarative machine learning on spark
2016
Cited alongside, same era.
http://aws.amazon.com/athena/
Amazon Athena
Cited in the paper.
https://github.com/awslabs/lambda-refarch-mapreduce
Serverless Reference Architecture: MapReduce
Cited in the paper.
Improving execution concurrency of large-scale matrix multiplication on distributed data-parallel platforms
2017
Later among the works it cites.
Occupy the cloud: distributed computing for the 99%
2017
Later among the works it cites.
Ray: A distributed framework for emerging ai applications
2017
Later among the works it cites.
Non-asymptotic analysis of robust control from coarse-grained identification
2017
Later among the works it cites.