6 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…
Resource-efficient Inference with Foundation Model Programs
Lunyiu Nie, Zhimin Ding, Kevin Yu +3
The inference-time resource costs of large language and vision models present a growing challenge in production deployments. We propose the use of foundation model programs, i.e.,…
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…
Online Cascade Learning for Efficient Inference over Streams
Lunyiu Nie, Zhimin Ding, Erdong Hu +2
Large Language Models (LLMs) have a natural role in answering complex queries about data streams, but the high computational cost of LLM inference makes them infeasible in many suc…