Tensor Contraction Engine: Abstraction and Automated Parallel Implementation of Configuration-Interaction, Coupled-Cluster, and Many-Body Perturbation Theories

By S. Hirata. 2003

384 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/a5eeff064660dd4e349f8e07f444b03e64f06678

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. J. Demmel
2. John F. Stanton
3. Minjie Wang
4. Chien-chin Huang
5. Devin A. Matthews
6. Edward F. Valeev
7. Edgar Solomonik
8. Jinyang Li

In Atomic and Molecular Physics, and Optics

1. John F. Stanton
2. J. Demmel
3. Devin A. Matthews
4. Edward F. Valeev
5. Edgar Solomonik
6. Jeff R. Hammond

In Spectroscopy

1. John F. Stanton
2. J. Demmel
3. Devin A. Matthews
4. Edward F. Valeev
5. Edgar Solomonik
6. Jeff R. Hammond

Citations in the corpus

• A massively parallel tensor contraction framework for coupled-cluster computations (2014, methodology): “We also discuss parallel numerical linear algebra algorithms which will serve as motivation and building blocks for the design of Cyclops Tensor Framework.”
• Scalable task-based algorithm for multiplication of block-rank-sparse matrices (2015, background): “Efficient application of operators to states — represented by matrix multiplication (tensor contraction) — demands taking advantage of the matrix (tensor) structure that take the form of (a) block-sparsity due to the distance decay of the operator kernel and the localized nature of basis functions [”
• Supporting Very Large Models using Automatic Dataflow Graph Partitioning (2018, background): “Cylops Tensor Framework [74] and Tensor Contraction Engine [75] are specialized systems for automatically parallelizing tensor contraction.”