A massively parallel tensor contraction framework for coupled-cluster computations

By Edgar Solomonik, Devin A. Matthews, Jeff R. Hammond, John F. Stanton, J. Demmel. 2014

205 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Hardware and Architecture, Spectroscopy

How researchers see this paper

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

In Atomic and Molecular Physics, and Optics

1. Edward F. Valeev

In Hardware and Architecture

1. Jinyang Li
2. Shoaib Kamil
3. Minjie Wang
4. Chien-chin Huang
5. Saman P. Amarasinghe

In Spectroscopy

1. Edward F. Valeev

Citations in the corpus

• Supporting Very Large Models using Automatic Dataflow Graph Partitioning (2018, methodology): “These libraries implement efficient parallel matrix algorithms [23, 62] and tensor operations [63].”
• Scalable task-based algorithm for multiplication of block-rank-sparse matrices (2015, background): “Keywords distributed memory, matrix multiplication, SUMMA, lowrank decomposition, irregular computation, rank-structured, matrix, H matrix, semiseparable matrix, task parallelism, tensor contraction”
• The tensor algebra compiler (2017, background): “Two recent efforts are libtensor with extensive support for tensor symmetries [Epifanovsky et al. 2013]; and CTF with a focus on distributed computations [Solomonik et al. 2014].”