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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

Quantum ESPRESSO toward the exascale.

Paolo Giannozzi¹, Oscar Baseggio², Pietro Bonfà³

¹Dipartimento di Scienze Matematiche, Informatiche e Fisiche, Università di Udine, via delle Scienze 206, I-33100 Udine, Italy, European Union.

The Journal of Chemical Physics

|April 24, 2020

Summary

This summary is machine-generated.

Quantum ESPRESSO, a materials modeling code, is being ported to hardware accelerators. This effort aims to overcome energy limitations for exascale computing.

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Area of Science:

Computational Materials Science
Quantum Mechanics
High-Performance Computing

Background:

Quantum ESPRESSO is a widely used open-source software for quantum-mechanical materials modeling.
It relies on density-functional theory, pseudopotentials, and plane waves.
Current hardware architectures face energy constraints for exascale computing.

Purpose of the Study:

To present the motivation for porting Quantum ESPRESSO to heterogeneous architectures.
To review the ongoing efforts in adapting the code for hardware accelerators.
To address energy efficiency challenges in materials modeling for exascale.

Main Methods:

Porting the Quantum ESPRESSO code to heterogeneous computing architectures.
Leveraging hardware accelerators (e.g., GPUs, FPGAs) for computation.
Benchmarking performance and energy consumption on diverse hardware.

Main Results:

Demonstration of Quantum ESPRESSO's adaptability to hardware accelerators.
Identification of performance gains and energy savings through acceleration.
Validation of the approach for future exascale systems.

Conclusions:

Porting Quantum ESPRESSO to hardware accelerators is crucial for advancing materials modeling.
This strategy will enable more energy-efficient and powerful simulations.
The work paves the way for achieving exascale computing in materials science.