Green Algorithms for Sustainable High-Performance Computing

  • Authors

    • Dr. Noorul Hasan Professor, University of Dhaka, Bangladesh Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2018PII3J8Y

    Published 11-02-2018

  • Green Computing, Sustainable HPC, Energy-Efficient Algorithms, Carbon-Aware Computation, Environmental Impact, Energy Optimization, Computational Sustainability, Algorithmic Efficiency, Power-Aware Programming, Low-Energy High-Performance Computing

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    Green Algorithms for Sustainable High-Performance Computing. (2018). International Journal of Modern Research in Science & Engineering, 1(2), 01-14. https://doi.org/10.67228/30716357/IJMRSE-2018PII3J8Y

  • Abstract

    High-Performance Computing (HPC) has become a cornerstone for scientific discovery, technological advancement, and industrial innovation. However, the environmental impact of large-scale computing infrastructures is increasingly a concern, with rising energy consumption and carbon emissions. This paper explores the concept of green algorithms—algorithmic strategies designed to reduce energy use, improve computational efficiency, and lower environmental footprints in HPC environments. We present a comprehensive review of current practices, emerging techniques, and optimization approaches that promote sustainability in algorithm design. The paper also analyzes case studies where energy-aware algorithm design significantly reduced resource usage, and proposes a framework for integrating sustainability metrics into algorithm development and benchmarking. Our goal is to foster a deeper integration of environmental responsibility into the core of computational research and development.

  • References

    [1] Malmodin, J., & Lundén, D. (2018). The energy and carbon footprint of the global ICT and E&M sectors 2010–2015. Sustainability, 10(9), 3027.

    [2] Fan, X., Weber, W. D., & Barroso, L. A. (2007). Power provisioning for a warehouse-sized computer. ACM SIGARCH Computer Architecture News, 35(2), 13–23.

    [3] Buyya, R., Beloglazov, A., & Abawajy, J. (2010). Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges. Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), 6, 1–12.

    [4] Varghese, B., & Buyya, R. (2018). Next generation cloud computing: New trends and research directions. Future Generation Computer Systems, 79, 849–861.

    [5] Ge, R., Feng, X., Song, S., Chang, H. C., Li, D., & Cameron, K. W. (2010). PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications. IEEE Transactions on Parallel and Distributed Systems, 21(5), 658–671.

    [6] Feng, W. C., & Cameron, K. W. (2007). The Green500 List: Encouraging Sustainable Supercomputing. IEEE Computer, 40(12), 50–55.

    [7] Knobloch, M., Hager, G., Wellein, G., & others. (2013). Energy-Aware High Performance Computing—A Survey. Advances in Computers, 88, 1–78.

    [8] Gillam, L., & Zakarya, M. (2017). Energy Efficient Computing, Clusters, Grids and Clouds: A Taxonomy and Survey. Sustainable Computing: Informatics and Systems, 14, 13–33.

    [9] Beloglazov, A., Buyya, R., Lee, Y. C., & Zomaya, A. Y. (2011). A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems. Advances in Computers, 82, 47–111.

    [10] Kliazovich, D., Bouvry, P., & Khan, S. U. (2012). GreenCloud: A Packet-Level Simulator of Energy-Aware Cloud Computing Data Centers. Journal of Supercomputing, 62(3), 1263–1283.

    [11] Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M. Q., & Pentikousis, K. (2010). Energy-Efficient Cloud Computing. The Computer Journal, 53(7), 1045–1051.

    [12] Gao, Y., Guan, H., Qi, Z., Hou, Y., & Liu, L. (2015). A Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement in Cloud Computing. Journal of Computer and System Sciences, 79(8), 1230–1242. (Widely cited for energy-aware resource allocation in HPC/cloud environments.)

  • Downloads