14 citations · 20 across the 8 of their papers we have counts for
8 papers
Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation
Yuxin Tang, Zhiyuan Xin, Zhimin Ding +4
A \emph{tensor-relational} computation is a relational computation where individual tuples carry vectors, matrices, or higher-dimensional arrays. An advantage of tensor-relational…
DOPPLER: Dual-Policy Learning for Device Assignment in Asynchronous Dataflow Graphs
Xinyu Yao, Daniel Bourgeois, Abhinav Jain +5
We study the problem of assigning operations in a dataflow graph to devices to minimize execution time in a work-conserving system, with emphasis on complex machine learning worklo…
EinDecomp: Decomposition of Declaratively-Specified Machine Learning and Numerical Computations for Parallel Execution
Daniel Bourgeois, Zhimin Ding, Dimitrije Jankov +6
We consider the problem of automatically decomposing operations over tensors or arrays so that they can be executed in parallel on multiple devices. We address two, closely-linked…
TURNIP: A "Nondeterministic" GPU Runtime with CPU RAM Offload
Zhimin Ding, Jiawen Yao, Brianna Barrow +7
An obvious way to alleviate memory difficulties in GPU-based AI computing is via CPU offload, where data are moved between GPU and CPU RAM, so inexpensive CPU RAM is used to increa…
Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat
Erdong Hu, Yuxin Tang, Anastasios Kyrillidis +1
We carefully evaluate a number of algorithms for learning in a federated environment, and test their utility for a variety of image classification tasks. We consider many issues th…
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
Yuxin Tang, Zhimin Ding, Dimitrije Jankov +3
The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally.…
Tensor Relational Algebra for Machine Learning System Design
Binhang Yuan, Dimitrije Jankov, Jia Zou +3
We consider the question: what is the abstraction that should be implemented by the computational engine of a machine learning system? Current machine learning systems typically pu…
Distributed Learning of Deep Neural Networks using Independent Subnet Training
Binhang Yuan, Cameron R. Wolfe, Chen Dun +3
Distributed machine learning (ML) can bring more computational resources to bear than single-machine learning, thus enabling reductions in training time. Distributed learning parti…