Ecological Impact of Green Computing Using Graphical Processing Units in Molecular Dynamics Simulations

Ecological Impact of Green Computing Using Graphical Processing Units in Molecular Dynamics Simulations

Izabele Marquetti (North Carolina A&T State University, USA), Jhonatam Rodrigues (North Carolina A&T State University, USA) and Salil S. Desai (North Carolina A&T State University, USA)
Copyright: © 2018 |Pages: 14
DOI: 10.4018/IJGC.2018010103

Abstract

Molecular dynamics (MD) models require comprehensive computational power to simulate nanoscale phenomena. Traditionally, central processing unit (CPU) clusters have been the standard method of performing these numerically intensive computations. This article investigates the use of graphical processing units (GPUs) to implement large-scale MD models for exploring nanofluidic-substrate interactions. MD models of water nanodroplets over flat silicon substrate are tracked wherein the simulation attains a steady state computational performance. Different classes of GPU units from NVIDIA (C2050, K20, and K40) are evaluated for energy efficiency performance with respect to three green computing measures: simulation completion time, power consumption, and CO2 emissions. The CPU+K40 configuration displayed the lowest energy consumption profile for all the measures. This research demonstrates the use of energy efficient graphical computing versus traditional CPU computing for high-performance molecular dynamics simulations.
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Introduction

Green Computing

Computing technologies are usually associated with high energy consumption. Thus, the energy efficiency of hardware and operating systems has become a financial and environmental concern (Zhu, Sun, & Hu, 2012). In the United States, desktop computers can represent around 10% of the consumption of the commercial electricity. Also, high-performance computers require a powerful cooling system for heat dissipation, thereby exacerbating the energy consumption. Consequently, computers can produce carbon dioxide equivalent to millions of cars (Li, 2012). The choice of software also influences the energy consumption profile and needs to be evaluated for power optimization purposes. Therefore, green practices are necessary to be incorporated into the design and operation of systems. Green computing is a recent approach which allows computer systems to be energy efficient by implementing newer technologies to reduce power consumption without compromising their performance (Binder & Suri, 2009; Singh, 2015; Zhou, Xiao, Yang, & Gong, 2016; Zhu et al., 2012). In addition, to improve the efficiency and economy of computer systems, green computing also considers the reduction of computer systems impact on the environment and people. These include the achievement of energy savings and environmental protection, which can impact the life cycle of computer systems (Zhang, Gong, & Li, 2011). Harmon and Auseklis (2009) define green computing as improving the efficiency of computing resources while minimizing the environmental impact, considering the entire life cycle of a product. This can be achieved by reducing the consumption of water, energy, limited resources and avoiding the use of hazardous materials, thereby minimizing all waste in the entire supply chain.

The practice of green computing involves the design, manufacture, use and proper disposal of computers and all subsystems, such as printers, monitors, and storage devices, in an efficient and effective way that causes minimal impact to the environment (Velte, Velte, & Elsenpeter, 2008). These can be achieved in our daily routine with simple actions, such as switching off the computer when not being used, choosing computer monitors that are more energy efficient (LED backlighting), keeping the computer well-maintained and optimized, disabling unnecessary programs that start automatically, recycling computer parts, batteries and printer cartridge (Appasami & Suresh Joseph, 2011; Chakraborty, Bhattacharyya, Nargiza, & Bedajna, 2009).

Green computing can be considered in terms of both software and hardware. In software, its objective is to obtain methods that increase the efficiency of programs, decreasing storage space and saving energy. Some examples include Cloud Computing, Distributed Computing, and High-Performance Computing. For hardware, green computing includes certain technologies that enable a significant reduction in the energy consumption and the emissions footprint, while increasing economic efficiency and facilitating recycling (Zhang et al., 2011).

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