640 citations · 1.3k across the 12 of their papers we have counts for
12 papers
Improving Runtime Performance of Tensor Computations using Rust From Python
Kimmie Harding, Daniel M. Dunlavy
In this work, we investigate improving the runtime performance of key computational kernels in the Python Tensor Toolbox (pyttb), a package for analyzing tensor data across a wide…
Computing Sparse Tensor Decompositions via Chapel and C++/MPI Interoperability without Intermediate I/O
S. Isaac Geronimo Anderson, Daniel M. Dunlavy
We extend an existing approach for efficient use of shared mapped memory across Chapel and C++ for graph data stored as 1-D arrays to sparse tensor data stored using a combination…
Analyzing the Performance Portability of Tensor Decomposition
S. Isaac Geronimo Anderson, Keita Teranishi, Daniel M. Dunlavy +1
We employ pressure point analysis and roofline modeling to identify performance bottlenecks and determine an upper bound on the performance of the Canonical Polyadic Alternating Po…
Tensor Decompositions for Count Data that Leverage Stochastic and Deterministic Optimization
Jeremy M. Myers, Daniel M. Dunlavy
There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decompos…
Zero-Truncated Poisson Regression for Sparse Multiway Count Data Corrupted by False Zeros
Oscar López, Daniel M. Dunlavy, Richard B. Lehoucq
We propose a novel statistical inference methodology for multiway count data that is corrupted by false zeros that are indistinguishable from true zero counts. Our approach consist…
Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software: Extended Analysis
Jeremy M. Myers, Daniel M. Dunlavy, Keita Teranishi +1
Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP…