arXiv subjects

Manish Kumar Mishra

Publications and source records attributed to Manish Kumar Mishra.

4 recordsLinked to original sources

arXiv2025

Tuning of Vectorization Parameters for Molecular Dynamics Simulations in AutoPas

Molecular Dynamics simulations can help scientists to gather valuable insights for physical processes on an atomic scale. This work explores various techniques for SIMD vectorization to improve the pairwise force calculation between molecules in the scope of the particle simulation library AutoPas. The focus lies on the order in which particle values are loaded into vector registers to achieve the most optimal performance regarding execution time or energy consumption. As previous work indicates that the optimal MD algorithm can change during runtime, this paper investigates simulation-specific parameters like particle density and the impact of the neighbor identification algorithms, which distinguishes this work from related projects. Furthermore, AutoPas' dynamic tuning mechanism is extended to choose the optimal vectorization order during runtime. The benchmarks show that considering different particle interaction orders during runtime can lead to a considerable performance improvement for the force calculation compared to AutoPas' previous approach.

cs.DC↗

arXiv2025

The Multiple Time-Stepping Method for 3-Body Interactions in High Performance Molecular Dynamics Simulations

Understanding the complex behavior of molecular systems is fundamental to fields such as physics, materials science, and biology. Molecular dynamics (MD) simulations are crucial tools for studying atomic-level dynamics. This work focuses on improving the efficiency of MD simulations involving two-body and three-body interactions. Traditional two-body potentials often can not fully capture the complexity of molecular systems, making the inclusion of three-body interactions important. However, these interactions are in a cubic complexity class, compared to a quadratic one for two-body interactions, and therefore are computationally expensive, even when a cutoff distance is applied. One way to improve efficiency is to use the r-RESPA multiple time-stepping algorithm to reduce the number of three-body interaction calculations. In this work, we investigate this method in the context of High Performance Computing (HPC) methods that parallelize the calculations. In particular, we investigate a communication-reducing distributed-memory parallel method from literature and present a novel shared-memory parallel cutoff method, implemented in the particle simulation library AutoPas. The results and methods are discussed, providing insights into potential advancements in MD simulation efficiency.

cs.CE↗

arXiv2025

Algorithm Selection in Short-Range Molecular Dynamics Simulations

Numerous algorithms and parallelisations have been developed for short-range particle simulations; however, none are optimally performant for all scenarios. Such a concept led to the prior development of the particle simulation library AutoPas, which implemented many of these algorithms and parallelisations and could select and tune these over the course of the simulation as the scenario changed. Prior works have, however, used only naive approaches to the algorithm selection problem, which can lead to significant overhead from trialling poorly performing algorithmic configurations. In this work, we investigate this problem in the case of Molecular Dynamics simulations. We present three algorithm selection strategies: an approach which makes performance predictions from past data, an expert-knowledge fuzzy logic-based approach, and a data-driven random forest-based approach. We demonstrate that these approaches can achieve speedups of up to 4.05 compared to prior approaches and 1.25 compared to a perfect configuration selection without dynamic algorithm selection. In addition, we discuss the practicality of the strategies in comparison to their performance, to highlight the tractability of such solutions.

cs.CE↗

arXiv2024

Advancing Vehicle Plate Recognition: Multitasking Visual Language Models with VehiclePaliGemma

License plate recognition (LPR) involves automated systems that utilize cameras and computer vision to read vehicle license plates. Such plates collected through LPR can then be compared against databases to identify stolen vehicles, uninsured drivers, crime suspects, and more. The LPR system plays a significant role in saving time for institutions such as the police force. In the past, LPR relied heavily on Optical Character Recognition (OCR), which has been widely explored to recognize characters in images. Usually, collected plate images suffer from various limitations, including noise, blurring, weather conditions, and close characters, making the recognition complex. Existing LPR methods still require significant improvement, especially for distorted images. To fill this gap, we propose utilizing visual language models (VLMs) such as OpenAI GPT4o, Google Gemini 1.5, Google PaliGemma (Pathways Language and Image model + Gemma model), Meta Llama 3.2, Anthropic Claude 3.5 Sonnet, LLaVA, NVIDIA VILA, and moondream2 to recognize such unclear plates with close characters. This paper evaluates the VLM's capability to address the aforementioned problems. Additionally, we introduce ``VehiclePaliGemma'', a fine-tuned Open-sourced PaliGemma VLM designed to recognize plates under challenging conditions. We compared our proposed VehiclePaliGemma with state-of-the-art methods and other VLMs using a dataset of Malaysian license plates collected under complex conditions. The results indicate that VehiclePaliGemma achieved superior performance with an accuracy of 87.6\%. Moreover, it is able to predict the car's plate at a speed of 7 frames per second using A100-80GB GPU. Finally, we explored the multitasking capability of VehiclePaliGemma model to accurately identify plates containing multiple cars of various models and colors, with plates positioned and oriented in different directions.

cs.CV↗