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@article{191745,
        author = {Sparsh},
        title = {Parallel Processing for Material Science Discovery: A Workflow Bottleneck Analysis and Optimization Framework},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {108-111},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191745},
        abstract = {Recent advances in material science increasingly rely on large-scale computational simulations, particularly in quantum chemistry, molecular modeling, electronic structure prediction, and high-throughput screening of novel materials. Traditional CPU-based pipelines struggle due to the immense computational cost of solving Schrödinger-based models, density functional theory (DFT) calculations, molecular dynamics (MD), and multi-physics simulations. Parallel GPU architectures, initially designed for graphics rendering, have demonstrated tremendous speed-ups for scientific computing because of their massively parallel execution units. However, despite the clear benefits, GPU-accelerated material science workflows continue to face critical bottlenecks—including memory bandwidth limitations, communication overhead, kernel inefficiencies, algorithm-hardware mismatch, and poor utilization of heterogeneous resources.
This research investigates: What are the bottlenecks in current GPU-accelerated material-science pipelines, and how can they be mitigated?
The paper includes: (1) an overview of GPU-based material computation, (2) bottleneck identification through case-study analysis, (3) workflow profiling, and (4) proposed optimizations across hardware, software, and algorithm layers. Results show that optimized kernel design, mixed-precision computing, asynchronous communication, and domain-specific GPU libraries significantly improve throughput. The study concludes with a generalized optimization framework for future GPU-driven material discovery systems.},
        keywords = {GPU Acceleration, Computational Materials Science, High-Performance Computing, DFT and Molecular Dynamics, Workflow Optimization, Parallel Computing},
        month = {},
        }