Accurate Sampling with Noisy Forces from Approximate Computing

Varadarajan Rengaraj,Thomas D Kühne,Christian Plessl,Michael Lass

doi:10.3390/computation8020039

Abstract

In scientific computing, the acceleration of atomistic computer simulations by means of custom hardware is finding ever-growing application. A major limitation, however, is that the high efficiency in terms of performance and low power consumption entails the massive usage of low precision computing units. Here, based on the approximate computing paradigm, we present an algorithmic method to compensate for numerical inaccuracies due to low accuracy arithmetic operations rigorously, yet still obtaining exact expectation values using a properly modified Langevin-type equation.

Highlights

Molecular dynamics (MD) is a very powerful and widely used technique to study thermodynamic equilibrium properties, as well as the real-time dynamics of complex systems made up of interacting atoms [1]
Besides algorithmic developments [7,8,9,10,11,12,13,14], there have been numerous custom computing efforts in this area to increase the efficiency of MD simulations by means of hardware acceleration, which we take up in this work
To demonstrate the concept of approximate computing, we introduce white noise to the interatomic forces that are computed while running the MD simulation

Summary

Introduction

Molecular dynamics (MD) is a very powerful and widely used technique to study thermodynamic equilibrium properties, as well as the real-time dynamics of complex systems made up of interacting atoms [1]. Besides algorithmic developments [7,8,9,10,11,12,13,14], there have been numerous custom computing efforts in this area to increase the efficiency of MD simulations by means of hardware acceleration, which we take up in this work Examples of the latter are MD implementations on graphics processing units (GPUs) [15,16,17,18,19,20,21], field-programmable gate arrays (FPGAs) [22,23], and application-specific integrated circuits (ASICs) [24,25]. To maximize the computational power for a given silicon

Methods

Results

Conclusion