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Optimizing Irregular Communication with Neighborhood Collectives and Locality-Aware Parallelism

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arXiv 2023年

作者： Collom, Gerald Li, Rui Peng Bienz, Amanda Department of Computer Science University of New Mexico Albuquerque United States Center for Advanced Scientific Computing Lawrence Livermore National Laboratory Livermore United States

Irregular communication often limits both the performance and scalability of parallel applications. Typically, applications individually implement irregular messages using point-to-point communications, and any optimizations are added directly into the application. As a result, these optimizations lack portability. There is no easy way to optimize point-to-point messages within MPI, as the interface for single messages provides no information on the collection of all communication to be performed. However, the persistent neighbor collective API, released in the MPI 4 standard, provides an interface for portable optimizations of irregular communication within MPI libraries. This paper presents methods for optimizing irregular communication within neighborhood collectives, analyzes the impact of replacing point-to-point communication in existing codebases such as Hypre BoomerAMG with neighborhood collectives, and finally shows an up to 1.32x speedup on sparse matrix-vector multiplication within a BoomerAMG solve through the use of our optimized neighbor collectives. The authors analyze multiple implementations of neighborhood collectives, including a standard implementation, which simply wraps standard point-to-point communication, as well as multiple implementations of locality-aware aggregation. All optimizations are available in an open-source codebase, MPI Advance, which sits on top of MPI, allowing for optimizations to be added into existing codebases regardless of the system MPI install. © 2023, CC BY.

关键词： Application programming interfaces (API)

Android malware detection and classification using stacked machine learning

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Android malware detection and classification using stacked m...

4th International Conference on Distributed Sensing and Intelligent Systems (ICDSIS 2023)

作者： F. Nawshin R. Gad D. Unal P. N. Suganthan KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar Department of Computer Science & Engineering College of Engineering Qatar University Doha Qatar

ISBN: (数字)9781837240258

The widespread use of Android smartphones in daily life can be attributed to the extensive prevalence stemming from the Android OS and the availability of opensource applications. People have become accustomed to performing various tasks, such as online banking transactions, scheduling appointments, attending virtual meetings and classes, and online shopping, on their mobile devices instead of computers. However, the significant use of mobile phones also presents opportunities for attackers to access sensitive and confidential information without the user's knowledge. Intruders often use malicious applications found on the Google Play Store and other third-party app markets. The increased volume of Android malware in recent years has severely threatened human privacy and security. In this paper, we present a novel solution that utilizes a stacking ensemble machine learning approach to detect whether an application is malware or benign, which achieved 98% accuracy and F1-Score, and to identify the malware category with 96% accuracy and F1-Score. We conducted the experiment on the CCCS-CIC-AndMal-2020 dataset, which comprises 27,125 samples. Static features such as permissions, meta-data, and intents were analyzed, and the performance of standard machine learning algorithms was compared to our proposed model. Our results demonstrate that the stacked ensemble model shows promising results for malware detection (binary classification) and malware type classification (multi-class classification).

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SEOpinion: Summarization and Exploration Opinion of E-Commerce Websites

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arXiv 2023年

作者： Mabrouk, Alhassan Díaz Redondo, Rebeca P. Kayed, Mohammed Mathematics and Computer Science Department Faculty of Science Beni-Suef University Egypt Information & Computing Lab AtlantTIC Research Center Telecommunication Engineering School Universidade de Vigo Spain Computer Science Department Faculty of Computers and Artificial Intelligence Beni-Suef University Egypt

E-Commerce (EC) websites provide a large amount of useful information that exceed human cognitive processing ability. In order to help customers in comparing alternatives when buying a product, previous studies designed opinion summarization systems based on customer reviews. They ignored templates’ information provided by manufacturers, although these descriptive information have much product aspects/characteristics. Therefore, this paper proposes a methodology coined as SEOpinion (Summarization and Exploration of Opinions) which provides a summary for the product aspects and spots opinion(s) regarding them, using a combination of templates’ information with the customer reviews in two main phases. First, the Hierarchical Aspect Extraction (HAE) phase creates a hierarchy of product aspects from the template. Subsequently, the Hierarchical Aspect-based Opinion Summarization (HAOS) phase enriches this hierarchy with customers’ opinions;to be shown to other potential buyers. To test the feasibility of using Deep Learning-based BERT techniques with our approach, we have created a corpus by gathering information from the top five EC websites for laptops. The experimental results show that Recurrent Neural Network (RNN) achieves better results (77.4% and 82.6% in terms of F1-measure for the first and second phase) than the Convolutional Neural Network (CNN) and the Support Vector Machine (SVM) technique. Copyright © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

Detection of Linguistic Bad Smells in GRL Models: An NLP Approach

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Detection of Linguistic Bad Smells in GRL Models: An NLP App...

Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)

作者： Nouf Alturayeif Jameleddine Hassine Information and Computer Science Department KFUPM Dhahran Saudi Arabia Computing Department Imam Abdurahman Bin Faisal University Dammam Saudi Arabia Information and Computer Science Department KFUPM Interdisciplinary Research Center for Intelligent Secure Systems KFUPM Dhahran Saudia Arabia

Goal-Oriented Requirements Engineering (GORE) plays a crucial role in facilitating effective communication between stakeholders in system development. Through the use of goal models, GORE provides a structured approach for eliciting, analyzing, and managing requirements from the perspective of stakeholders' goals and intentions. However, goal models are susceptible to poor practices, called also bad smells, that may hinder the effective communication and understanding among stakeholders, potentially leading to misinterpretations and inconsistencies in requirements. In particular, goal models are prone to linguistic bad smells encompassing a spectrum of anoma-lies such as unclear or ambiguous goal statements, conflicting or contradictory requirements, and instances of misspellings. Therefore, identifying and addressing linguistic bad smells in goal models is crucial for ensuring the quality and accuracy of goal models. In this paper, we define seventeen linguistic bad smells in goal models, classified into four categories: Syntax, Semantics, Pragmatics, and Complexity. Furthermore, we provide Natural Language Processing (NLP) based detection methods for twelve specific bad smells, which we have automated to target Textual GRL (TGRL) models. The proposed approach and tool are evaluated using two TGRL models achieving an F2-Score of 0.8.

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A Two-level GPU-Accelerated Incomplete LU Preconditioner for General Sparse Linear Systems

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arXiv 2023年

作者： Xu, Tianshi Li, Ruipeng Osei-Kuffuor, Daniel Department of Computer Science and Engineering University of Minnesota Minnesota United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory CA United States

This paper presents a parallel preconditioning approach based on incomplete LU (ILU) factorizations in the framework of Domain Decomposition (DD) for general sparse linear systems. We focus on distributed memory parallel architectures, specifically, those that are equipped with graphic processing units (GPUs). In addition to block Jacobi, we present general purpose two-level ILU Schur complement-based approaches, where different strategies are presented to solve the coarse-level reduced system. These strategies are combined with modified ILU methods in the construction of the coarse-level operator, in order to effectively remove smooth errors. We leverage available GPU-based sparse matrix kernels to accelerate the setup and the solve phases of the proposed ILU preconditioner. We evaluate the efficiency of the proposed methods as a smoother for algebraic multigrid (AMG) and as a preconditioner for Krylov subspace methods, on challenging anisotropic diffusion problems and a collection of general sparse *** Codes G.1.3 Copyright © 2023, The Authors. All rights reserved.

关键词： Graphics processing unit

3D Vessel Graph Generation Using Denoising Diffusion

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arXiv 2024年

作者： Prabhakar, Chinmay Shit, Suprosanna Musio, Fabio Yang, Kaiyuan Amiranashvili, Tamaz Paetzold, Johannes C. Li, Hongwei Bran Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Switzerland Center for Computational Health ZHAW Switzerland Department of Computer Science Technical University of Munich Germany Department of Computing Imperial College London United Kingdom Athinoula A. Martinos Center Harvard Medical School United States

Blood vessel networks, represented as 3D graphs, help predict disease biomarkers, simulate blood flow, and aid in synthetic image generation, relevant in both clinical and pre-clinical settings. However, generating realistic vessel graphs that correspond to an anatomy of interest is challenging. Previous methods aimed at generating vessel trees mostly in an autoregressive style and could not be applied to vessel graphs with cycles such as capillaries or specific anatomical structures such as the Circle of Willis. Addressing this gap, we introduce the first application of denoising diffusion models in 3D vessel graph generation. Our contributions include a novel, two-stage generation method that sequentially denoises node coordinates and edges. We experiment with two real-world vessel datasets, consisting of microscopic capillaries and major cerebral vessels, and demonstrate the generalizability of our method for producing diverse, novel, and anatomically plausible vessel graphs. © 2024, CC BY.

关键词： Blood vessels

Performance-Aligned LLMs for Generating Fast Code

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arXiv 2024年

作者： Nichols, Daniel Polasam, Pranav Menon, Harshitha Marathe, Aniruddha Gamblin, Todd Bhatele, Abhinav Department of Computer Science University of Maryland College ParkMD United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Livermore Computing Lawrence Livermore National Laboratory LivermoreCA United States

Optimizing scientific software is a difficult task because codebases are often large and complex, and performance can depend upon several factors including the algorithm, its implementation, and hardware among others. Causes of poor performance can originate from disparate sources and be difficult to diagnose. Recent years have seen a multitude of work that use large language models (LLMs) to assist in software development tasks. However, these tools are trained to model the distribution of code as text, and are not specifically designed to understand performance aspects of code. In this work, we introduce a reinforcement learning based methodology to align the outputs of code LLMs with performance. This allows us to build upon the current code modeling capabilities of LLMs and extend them to generate better performing code. We demonstrate that our finetuned model improves the expected speedup of generated code over base models for a set of benchmark tasks from 0.9 to 1.6 for serial code and 1.9 to 4.5 for OpenMP code. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

Model Ownership Protection for Healthcare Consumer Electronics in Federated Edge Learning

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IEEE Transactions on Consumer Electronics 2024年

作者： Wang, Rong Liang, Junchuan Feng, Chaosheng Chakraborty, Chinmay Alqahtani, Fayez Yu, Keping Sichuan Normal University College of Computer Science Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Chengdu610068 China Birla Institute of Technology Mesra India King Saud University College of Computer and Information Sciences Software Engineering Department Riyadh12372 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan

With the rising demand for intelligent services and privacy protection in consumer artificial intelligence (AI), federated edge learning has emerged as a beacon for privacy-preserving distributed machine learning. This non-centralized approach offers distinct data privacy advantages, making it especially suitable for applications with strict privacy policies, such as consumer-centric healthcare systems. However, consumer electronics/devices in federated edge learning might unlawfully violate model ownership by duplicating or distributing models. Another concern is that a central server, if compromised, can become a conduit for model leakage, whether for profit motives or due to external intrusions, thus exacerbating the model ownership violation. To our knowledge, few works have focused on model ownership protection for federated edge learning in the consumer-centric healthcare domain. In this paper, we introduce a federated model ownership protection (FedMOP) approach. FedMOP is a model ownership safeguard and a formidable fortress built upon two core innovations. First, FedMOP can swiftly embed and replace watermarks during local training, allowing for the accurate identification and subsequent flagging of deceitful consumer devices. Second, FedMOP ensures that the server only accesses encrypted versions of the federated model, keeping the model a secret. Extensive evaluations demonstrate the robustness and effectiveness of FedMOP. We hope that FedMOP can light the way for surmounting similar ethical challenges in the broader realm of consumer-edge AI. © 1975-2011 IEEE.

关键词： ethical AI Federated edge learning healthcare consumer electronicss model ownership protection

Dual Multi-Atlas Representation Alignment for Brain Disorder Diagnosis Using Morphological Connectome 22

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Dual Multi-Atlas Representation Alignment for Brain Disorder...

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Han, Kangfu Hu, Dan Cheng, Jiale Liu, Tianming Bozoki, Andrea Zhu, Dajiang Li, Gang University of North Carolina at Chapel Hill Biomedical Research Imaging Center Department of Radiology Chapel HillNC United States School of Computing University of Georgia AthensGA United States University of North Carolina at Chapel Hill Department of Neurology Chapel HillNC United States University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX United States

ISBN: (纸本)9798331520526

In structural magnetic resonance imaging (MRI), morpholog-ical connectome plays an important role in capturing coordi-nated patterns of region-wise morphological features for brain disorder diagnosis. However, significant challenges remain in aggregating diverse representations from multiple brain at-lases, stemming from variations in the definition of regions of interest. To effectively integrate complementary information from multiple atlases while mitigating possible biases, we propose a novel dual multi-atlas representation alignment approach (DMAA) for brain disorder diagnosis. Specifically, we first minimize the maximum mean discrepancy of multi-atlas representations to align them into a unified distribution, reducing inter-atlas variability and enhancing effective feature fusion. Then, to further manage the anatomical variability, we apply optimal transport to capture and harmonize region-wise differences, preserving plausible relationships across at-lases. Extensive experiments on ADNI, PPMI, ADHD200, and SchizConnect datasets demonstrate the effectiveness of our proposed DMAA on brain disorder diagnosis using multi-atlas morphological connectome. © 2025 IEEE.

关键词： Neuroimaging