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GPU-Accelerated DNS of Compressible Turbulent Flows

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arXiv 2022年

作者： Kim, Youngdae Ghosh, Debojyoti Constantinescu, Emil M. Balakrishnan, Ramesh Computational Science Division Argonne National Laboratory 9700 S. Cass Ave. LemontIL60439 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 East Ave. LivermoreCA94550 United States Mathematics and Computer Science Division Argonne National Laboratory 9700 S. Cass Ave. LemontIL60439 United States

This paper explores strategies to transform an existing CPU-based high-performance computational fluid dynamics solver, HyPar, for compressible flow simulations on emerging exascale heterogeneous (CPU+GPU) computing platforms. The scientific motivation for developing a GPU-enhanced version of HyPar is to simulate canonical turbulent flows at the highest res-MSC Codes 76F65, 65Y05, 76F05, 35Q30, 65M06 Copyright © 2022, The Authors. All rights reserved.

关键词： Turbulent flow

Lecture Notes in Networks and Systems

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Preface

Lecture Notes in Networks and Systems 2024年 995卷 v-vii页

作者： Pant, Millie Nagar, Atulya Deep, Kusum Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand Roorkee India School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool United Kingdom Department of Mathematics Indian Institute of Technology Roorkee Uttarakhand Roorkee India

Mathematical modeling and simulation of magnetized bioconvective nanoliquid flow capturing Brownian motion, multiple slip, thermophoresis and gyrotactic microorganisms configured by rotating disk

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Partial Differential Equations in applied Mathematics 2024年 12卷

作者： Garalleh, Hakim AL Khan, Sami Ullah Waqas, M. Zamri, Nurnadiah Abdullaeva, Barno Gupta, Manish Department of Mathematical Science College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia Department of Mathematics Namal University Mianwali 42250 Pakistan NUTECH School of Applied Sciences and Humanities National University of Technology Islamabad 44000 Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Faculty of Informatics and Computing University Sultan Zainal Abidin Besut Campus Terengganu Besut 22200 Malaysia Department of Mathematics and Information Technologies Vice-Rector for Scientific Affairs Tashkent State Pedagogical University Tashkent Uzbekistan Division of Research and Development Lovely Professional University Phagwara India

This analysis describes the bioconvective flow of nanofluid due to rotation of disk. A uniform suspension of nanofluid between microorganisms is considered to analyze the applications of bioconvection. The nanofluid assumed to be electrically conducting with amplification of magnetic force. The problem is entertained in presence of different slip features including velocity, temperature, concentration and microorganisms. The formulation of problem in simplified form is attained via dimensionless variables. Shooting numerical scheme is used to compute the simulations. Physical interpretation and visualization of results is observed in view of parameters. The observations concluded that interaction of slip effects reduces the velocity profile but enhances nanofluid temperature and concentration profiles. The temperature profile increases with thermophoresis parameter. Current results comprise applications in cooling of electronics devices, thin film coating, gas turbines engine, energy systems etc. © 2024

关键词： Bioconvection Multiple slip effects Nanoparticles Numerical computations Rotating disks

Recorder 2.0: Efficient Parallel I/O Tracing and Analysis

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Recorder 2.0: Efficient Parallel I/O Tracing and Analysis

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Chen Wang Jinghan Sun Marc Snir Kathryn Mohror Elsa Gonsiorowski Department of Computer Science University of Illinois at Urbana-Champaign Urbana USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore USA

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Recorder is a multi-level I/O tracing tool that captures HDF5, MPI-I/O, and POSIX I/O calls. In this paper, we present a new version of Recorder that adds support for most metadata POSIX calls such as stat, link, and rename. We also introduce a compressed tracing format to reduce trace file size and run time overhead incurred from collecting the trace data. Moreover, we add a set of post-mortem and visualization routines to our new version of Recorder that manage the compressed trace data for users. Our experiments with four HPC applications show a file size reduction of over 2× and reduced post-processing time by 20% when using our new compressed trace file format.

关键词： Tools Libraries Optimization Data visualization Metadata Instruments Standards

Metall: A Persistent Memory Allocator For Data-Centric Analytics

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arXiv 2021年

作者： Iwabuchi, Keita Youssef, Karim Velusamy, Kaushik Gokhale, Maya Pearce, Roger Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 East Avenue LivermoreCA94550 United States Department of Computer Science Virginia Polytechnic Institute and State University BlacksburgVA24061 United States Department of Computer Science University of Maryland Baltimore County 1000 Hilltop Cir BaltimoreMD21250 United States

Data analytics applications transform raw input data into analytics-specific data structures before performing analytics. Unfortunately, such data ingestion steps are often more expensive than analytics. In addition, various types of NVRAM devices are already used in many HPC systems today. Such devices will be useful for storing and reusing data structures beyond a single process life cycle. We developed Metall, a persistent memory allocator built on top of the memory-mapped file mechanism. Metall enables applications to transparently allocate custom C++ data structures into various types of persistent memories. Metall incorporates a concise and high-performance memory management algorithm inspired by Supermalloc and the rich C++ interface developed by *** library. On a dynamic graph construction workload, Metall achieved up to 11.7x and 48.3x performance improvements over *** and memkind (PMEM kind), respectively. We also demonstrateMetall's high adaptability by integratingMetall into a graph processing framework, GraphBLAS Template Library. This study's outcomes indicate that Metall will be a strong tool for accelerating future large-scale data analytics by allowing applications to leverage persistent memory efficiently. Copyright © 2021, The Authors. All rights reserved.

关键词： Data structures

Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

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Journal of Healthcare Engineering 2022年第1期2022卷 3861161页

作者： Alzu'Bi, Dalia Abdullah, Malak Hmeidi, Ismail Alazab, Rami Gharaibeh, Maha El-Heis, Mwaffaq Almotairi, Khaled H. Forestiero, Agostino Hussein, Ahmad Mohdaziz Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid2210 Jordan Department of General Surgery and Urology University of Science and Technology Irbid22110 Jordan Department of Diagnostic and Interventional Radiology Faculty of Medicine Jordan University of Science and Technology Irbid2210 Jordan Computer Engineering Department Computer and Information Systems College Umm Al-Qura University Makkah21955 Saudi Arabia Institute for High Performance Computing and Networking CNR CS Rende Italy Deanship of E-Learning and Distance Education Umm Al-Qura University Makkah21955 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models;three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results;they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life. © 2022 Dalia Alzu'bi et al.

关键词： Convolutional neural networks

A directional regularization method for the limited-angle Helsinki Tomography Challenge using the Core Imaging Library (CIL)

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arXiv 2023年

作者： Jørgensen, Jakob Sauer Papoutsellis, Evangelos Murgatroyd, Laura Fardell, Gemma Pasca, Edoardo Department of Applied Mathematics and Computer Science Technical University of Denmark. Richard Petersens Plads Building 324 Kgs. Lyngby2800 Denmark Department of Mathematics The University of Manchester Oxford Road Alan Turing Building ManchesterM13 9PL United Kingdom Finden Ltd Rutherford Appleton Laboratory Harwell Campus DidcotOX11 0QX United Kingdom Scientific Computing Department Science & Technology Facilities Council Rutherford Appleton Laboratory Harwell Campus DidcotOX11 0QX United Kingdom

This article presents the algorithms developed by the Core Imaging Library (CIL) developer team for the Helsinki Tomography Challenge 2022. The challenge focused on reconstructing 2D phantom shapes from limited-angle computed tomography (CT) data. The CIL team designed and implemented five reconstruction methods using CIL (https://***/cil/), an open-source Python package for tomographic imaging. The CIL team adopted a model-based reconstruction strategy, unique to this challenge with all other teams relying on deep-learning techniques. The CIL algorithms showcased exceptional performance, with one algorithm securing the third place in the competition. The best-performing algorithm employed careful CT data pre-processing and an optimization problem with single-sided directional total variation regularization combined with isotropic total variation and tailored lower and upper bounds. The reconstructions and segmentations achieved high quality for data with angular ranges down to 50 degrees, and in some cases acceptable performance even at 40 and 30 degrees. This study highlights the effectiveness of model-based approaches in limited-angle tomography and emphasizes the importance of proper algorithmic design leveraging on available prior knowledge to overcome data limitations. Finally, this study highlights the flexibility of CIL for prototyping and comparison of different optimization methods.1MSC Codes 65R32, 94A08, 65K10 © 2023, CC BY.

关键词： computerized tomography

Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning

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arXiv 2022年

作者： Palmitessa, Rocco Grum, Morten Engsig-Karup, Allan Peter Löwe, Roland Technical University of Denmark Department of Environmental and Resources Engineering Section of Climate and Monitoring Miljøvej B115 Kgs. Lyngby2800 Denmark WaterZerv Fjenneslevvej 23 st. Brønshøj2700 Denmark Technical University of Denmark Department of Applied Mathematics and Computer Science Section of Scientific Computing Asmussens Allé 303B Kgs. Lyngby2800 Denmark

We propose and demonstrate a new approach for fast and accurate surrogate modelling of urban drainage system hydraulics based on physics-guided machine learning. The surrogates are trained against a limited set of simulation results from a hydrodynamic (HiFi) model. Our approach reduces simulation times by one to two orders of magnitude compared to a HiFi model. It is thus slower than e.g. conceptual hydrological models, but it enables simulations of water levels, flows and surcharges in all nodes and links of a drainage network and thus largely preserves the level of detail provided by HiFi models. Comparing time series simulated by the surrogate and the HiFi model, R2 values in the order of 0.9 are achieved. Surrogate training times are currently in the order of one hour. However, they can likely be reduced through the application of transfer learning and graph neural networks. Our surrogate approach will be useful for interactive workshops in initial design phases of urban drainage systems, as well as for real time applications. In addition, our model formulation is generic and future research should investigate its application for simulating other water systems. © 2022, CC BY-NC-ND.

关键词： Machine learning

DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer