Computational Physics on Graphics Processing Units

By Ari Harju, Topi Siro, Filippo Federici Canova, Samuli Hakala, Teemu Rantalaiho. 2012

25 citations recorded by the source corpus.

The use of graphics processing units for scientific computations is an emerging strategy that can significantly speed up various algorithms. In this review, we discuss advances made in the field of computational physics, focusing on classical molecular dynamics and quantum simulations for electronic structure calculations using the density functional theory, wave function techniques and quantum field theory.

Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture, ML Systems & Hardware, AI for Physical Sciences

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. Alexandra Sasha Luccioni
2. Aldo H. Romero
3. Matthieu J. Verstraete
4. Eric Bousquet
5. Alexandre Lacoste
6. Aimee van Wynsberghe
7. Anne-Laure Ligozat
8. Han-Jia Ye

In ML Systems & Hardware

1. Thomas Dandres
2. Alexandra Sasha Luccioni
3. Alexandre Lacoste
4. Victor Schmidt

In AI for Physical Sciences

1. Xavier Gonze
2. Alexandra Sasha Luccioni

Citations in the corpus

• Quantifying the Carbon Emissions of Machine Learning (2019, background): “In terms of performance the number of floating point operations per second (FLOPS) of GPUs has been steadily increasing in recent years, from 100 Giga FLOPS per second in 2004 to up to 15 Tera FLOPS per second in recent hardware [6].”
• Recent developments in the ABINIT software package (2016, methodology): “The resulting performances of ABINIT, when used on GPU, are similar to other DFT GPU implementations [117, 118, 119]: compared to a run on a single CPU, the GPU version of ABINIT (with as many GPUs as CPUs) is accelerated by a factor 3 to 5, depending on the computational load.”