Daniel Bourgeois
9 papers 39 citations h-index 1 2015–2026
Tensor Relational Algebra for Machine Learning System Design
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 push whole tensors...
Higher-level parallelization for local and distributed asynchronous task-based programming
DOPPLER: Dual-Policy Learning for Device Assignment in Asynchronous Dataflow Graphs
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 workloads. Prior...
EinDecomp: Decomposition of Declaratively-Specified Machine Learning and Numerical Computations for Parallel Execution
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 questions....
TURNIP: A "Nondeterministic" GPU Runtime with CPU RAM Offload
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 increase the amount of...
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
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. We show...
Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation
A \emph{tensor-relational} computation is a relational computation where individual tuples carry vectors, matrices, or higher-dimensional arrays. An advantage of tensor-relational computation is...