High-Performance Computing Approaches for Scientific Simulations

  • Authors

    • Dr. Lei Weing Department of Data Science, Tsinghua University, Beijing, China. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2021PII4S2K

    Published 11-05-2021

  • High-Performance Computing, Scientific Simulations, Parallel Computing, GPU Acceleration, Exascale Systems, Numerical Modeling

    Issue

    Vol. 4 No. 2 (2021)

    Section

    Articles

    How to Cite

    [1]
    L. Weing, “High-Performance Computing Approaches for Scientific Simulations”, IJADSMC, vol. 4, no. 2, pp. 01–14, Nov. 2021, doi: 10.67228/30713498/IJADSMC-2021PII4S2K.

  • Abstract

    High-Performance Computing (HPC) has become essential for modern scientific modeling, enabling large-scale simulations in fields such as climate science, fluid dynamics, molecular studies, astrophysics, and biomedical engineering. These applications demand high computational power, accuracy, and efficient parallel performance, which traditional computing cannot support. This paper examines HPC simulation methods, focusing on architectural evolution from vector supercomputers to parallel, heterogeneous, and exascale systems. It reviews key technologies such as distributed and shared memory systems, accelerators, and high-speed interconnects, along with techniques like domain decomposition, message passing, and hybrid parallelism. Recent advances in GPU acceleration, adaptive mesh refinement, parallel I/O, and energy-efficient computing are also discussed. The proposed framework integrates algorithm-level parallelization, hardware-aware optimization, and scalable software design. Performance analysis highlights improvements in speed, efficiency, scalability, and energy usage, demonstrating the effectiveness of optimized HPC approaches. The paper concludes with future challenges in exascale computing, AI-driven simulations, and sustainable HPC development.

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