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Towards Parallel Methods in Birational Geometry 8th

Towards Parallel Methods in Birational Geometry

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8th International conference on Mathematical Software (ICMS)

作者： Mirgain, Benjamin Univ Saarland Fachbereich Math Saarbrucken Germany Fraunhofer ITWM Div High Performance Comp Kaiserslautern Germany

ISBN: (纸本)9783031645280;9783031645297

Computational birational geometry is one of the key playing fields in an algorithmic approach to algebraic geometry, since birational maps are the fundamental way to relate algebraic varieties (or schemes). An important application is an algorithmic approach to the Minimal Model program (MMP), which aims to classify algebraic varieties with mild singularities by finding simple birational models of such varieties in their birational equivalence class. This note presents work towards parallel methods to solve problems in birational geometry. Making use of a representation of algebraic schemes in terms of charts allows for a parallel computational approach for handling both the varieties and rational maps between them. In this note, we illustrate this approach on examples.

关键词： Birational geometry schemes covered schemes parallel algorithms MMP

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MASKSTR: GUIDE SCENE TEXT RECOGNITION MODELS WITH MASKING 49

MASKSTR: GUIDE SCENE TEXT RECOGNITION MODELS WITH MASKING

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49th IEEE International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wei, Baole He, Minghang Gao, Liangcai Zhou, Duoyou Bai, Xiang Tang, Zhi Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China ByteDance Inc Beijing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Text recognition in information loss scenarios like blurriness, occlusion, and perspective distortion is challenging in real-world applications. To enhance robustness, some studies use extra unlabeled data for encoder pretraining. Others focus on improving decoder context reasoning. However, pretraining methods require abundant unlabeled data and high computing resources, while decoder-based approaches risk over-correction. In this paper, we propose MaskSTR, a dual-branch training framework for STR models, using patch masking to simulate information loss. MaskSTR guides visual representation learning, improving robustness to information loss conditions without extra data or training stages. Furthermore, we introduce Block Masking, a novel and straightforward mask generation method, for further performance enhancement. Experiments demonstrate MaskSTR's effectiveness across CTC, attention, and Transformer decoding methods, achieving significant performance gains and setting new state-of-the-art results.

关键词： Scene text recognition OCR computer vision

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FDSE: Enhance Symbolic Execution by Fuzzing-based Pre-Analysis (Competition Contribution) 27th

FDSE: Enhance Symbolic Execution by Fuzzing-based Pre-Analys...

引用

27th International conference on Fundamental Approaches to Software Engineering (FASE)

作者： Zhang, Guofeng Shuai, Ziqi Ma, Kelin Liu, Kunlin Chen, Zhenbang Wang, Ji Natl Univ Def Technol Coll Comp Changsha Peoples R China Natl Univ Def Technol State Key Lab Complex & Crit Software Environm Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha Peoples R China

ISBN: (纸本)9783031572586;9783031572593

FDSE serves as an automatic test generation tool designed for C programs based on symbolic execution. FDSE employs fuzzing-based pre-analysis and combines static symbolic execution and dynamic symbolic execution to improve the effectiveness of test generation. FDSE achieves 5132 scores and is ranked 4th in the branch coverage track of Test-Comp 2024.

关键词： Symbolic Execution Fuzzing Test-Case Generation

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Exploring Natural Language Processing Model Acceleration in Molecular Dynamics Simulation Using high-performance Computing and Machine Learning

Exploring Natural Language Processing Model Acceleration in ...

引用

2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Du, Qi Wang, Feng Huang, Hui Wan, Heng Wu, Xiaoyu Wu, Chengkun Hunan University College of Computer Science and Electronic Engineering Hunan Changsha China Hunan Institute of Scientific and Technical Information Hunan Changsha China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment Hunan Changsha China

ISBN: (纸本)9798350386226

In molecular dynamics simulations, the integral methods used to calculate molecular trajectories, such as the Verlet integral method, involve significant computational costs. On the premise of ensuring the accuracy of simulation, how to effectively reduce the amount of computation is always a challenging problem. This paper applies Natural Language Processing models to simulate molecular motion trajectories in molecular dynamics, integrating the MPI programming model and machine learning on a high-performance computing platform to achieve MPI parallelization during model training and inference. The results indicate that different types of neural network architectures have a significant impact on inference performance and accuracy. When using the deep spatio-temporal networks model, the mean absolute error is 0.0032. At an atomic scale of 3M, the parallel efficiency with 1024 computational nodes exceeds 90%, demonstrating excellent parallel performance. © 2024 IEEE.

关键词： Natural language processing systems

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Persistent and Partitioned MPI for Stencil Communication

Persistent and Partitioned MPI for Stencil Communication

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2024 IEEE high performance Extreme Computing conference, HPEC 2024

作者： Collom, Gerald Burmark, Jason Pearce, Olga Bienz, Amanda University of New Mexico Department of Computer Science Albuquerque United States Lawrence Livermore National Lab Livermore United States

ISBN: (纸本)9798350387131

Many parallel applications rely on iterative stencil operations, whose performance are dominated by communication costs at large scales. Several MPI optimizations, such as persistent and partitioned communication, reduce overheads and improve communication efficiency through amortized setup costs and reduced synchronization of threaded sends. This paper presents the performance of stencil communication in the Comb benchmarking suite when using non-blocking, persistent, and partitioned communication routines. The impact of each optimization is analyzed at various scales. Further, the paper presents an analysis of the impact of process count, thread count, and message size on partitioned communication routines. Measured timings show that persistent MPI communication can provide a speedup of up to 37 % over the baseline MPI communication, and partitioned MPI communication can provide a speedup of up to 68 %. © 2024 IEEE.

关键词： Benchmarking

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Hynify: A high-throughput and Unified Accelerator for Multi-Mode Nonparametric Statistics 24

Hynify: A High-throughput and Unified Accelerator for Multi-...

引用

61st ACM/IEEE Design Automation conference, DAC 2024

作者： Huang, Kaihong Shen, Dian Wang, Zhaoyang Yang, Juntao Wang, Beilun Department of Computer Science and Engineering Southeast University China

ISBN: (纸本)9798400706011

Nonparametric statistics methods are a class of robust and potent machine learning operators, which are widely used in various domains such as finance, medicine, and computer science. Such methods deliver an accurate estimation without an assumed data distribution. Moreover, they can handle discrete data with various data sources. Despite their desirable features, the calculation of large-scale nonparametric statistics is both compute- and memory-intensive, and the performance overhead hinders them from widespread *** paper identifies that the key performance bottleneck lies in the rank-based operations which are intensively involved in variants of nonparametric statistics methods. These rank-based operations can thereby be fully accelerated and structurally reused among diverse statistics. We then introduce Hynify, a high-throughput and unified accelerator that facilitates a rich set of nonparametric statistics. To ensure comprehensiveness, we capture three primary computational paradigms of nonparametric statistical methods, namely, aggregation, pair-wise rank, and concordance, with the right architecture designs. To improve throughput, Hynify exploits fine-grained computation and pipelining for increased performance. We implement Hynify in FPGA demonstration and representative experimental results demonstrate that Hynify delivers up to 160x/21x throughput improvement over GPU and 64-core CPU, respectively, while achieving up to 781×/62× energy efficiency improvement. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： program processors

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Statistical Analysis for Evaluation and Improvement of computer Science Education 21

Statistical Analysis for Evaluation and Improvement of Compu...

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21st International Learning and Technology conference, L and T 2024

作者： Elkafrawy, Passent Kamal, Ahmed Medhat, Walaa El-Hadidi, Mohamed Yousef, Ahmed Hassan Effat University ECoE Saudi Arabia Nile University Itcs Egypt Nile University Itcs Center for Informatics Science Giza Egypt Nile University Itcs Giza Egypt Benha University Fcai Egypt University of Birmingham ICGS Medical and Dental Sciences Dubai United Arab Emirates Egypt University of Informatics Cse Ain Shams University Cairo Egypt

ISBN: (纸本)9798350393569

Developing well-prepared and competent graduates is one of the main goals of all university programs globally. Recently, computer Science (CS) has achieved tremendous success among all career fields driven by the strong competence in the market and the rapid changes in technology. Our goal is to develop an automated framework that provides efficient management, evaluation and improvement of the CS students education, as well as a profound establishment of a successful study tree for CS university programs. Such a challenging goal comprises major factors that should be inclusively considered. high school (HS) students are expected to join CS university programs with different educational backgrounds and learning capabilities. The strength of association among several performance-related factors including the academic performance of students in HS is evaluated to gain insights and infer indicators in CS programs. The automatic correlational analysis of the prerequisites for each course is also investigated to assess the program structure and dependencies among several CS courses. In this comprehensive study, all these factors are efficiently analyzed in order to investigate the valid causes of low and high performance of both CS university students and programs. Experimental results have concluded several major findings with validated associations that assure and prioritize the importance of evaluation and improvement of CS education. © 2024 IEEE.

关键词： Curricula

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A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

A Scalable Real-Time Data Assimilation Framework for Predict...

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2024 Workshops of the International conference for high performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Yin, Junqi Liang, Siming Liu, Siyan Bao, Feng Chipilski, Hristo G. Lu, Dan Zhang, Guannan Oak Ridge National Laboratory National Center for Computational Science Oak RidgeTN United States Florida State University Department of Mathematics TallahasseeFL United States Oak Ridge National Laboratory Computational Science and Engineering Division Oak RidgeTN United States Florida State University Department of Scientific Computing TallahasseeFL United States Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States

ISBN: (纸本)9798350355543

AI-based foundation models like FourCastNet, GraphCast, ClimaX, and Pangu-Weather are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones and atmospheric rivers. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, e.g., the Local Ensemble Transform Kalman Filter (LETKF);and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions. Even though the proposed framework is tested only on a benchmark surface quasi-geostrophic (SQG) turbulence system, it has the potential to be combined with existing AI-based foundation models, making it suitable for future operational implementations. © 2024 IEEE.

关键词： data assimilation Diffusion models foundation models generative AI high-performance computing uncertainty quantification

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IRIS-GNN: Leveraging Graph Neural Networks for Scheduling on Truly Heterogeneous Runtime Systems

IRIS-GNN: Leveraging Graph Neural Networks for Scheduling on...

引用

2024 Workshops of the International conference for high performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Johnston, Beau De Boissiere, Thibault Monil, Mohammad Alaul Haque Miniskar, Narasinga Rao Young, Aaron Lee, Seyong Vetter, Jeffrey S. Oak Ridge National Laboratory Oak RidgeTN United States Canva Incorporated SydneyNSW Australia

ISBN: (纸本)9798350355543

The diversity of accelerators in computer systems poses significant challenges for software developers, such as managing vendor-specific compiler toolchains, code fragmentation requiring different kernel implementations, and performance portability issues. To address these, the Intelligent Runtime System (IRIS) was developed. IRIS works across various systems, from smartphones to supercomputers, enabling automatic performance scaling based on available accelerators. It introduces abstract tasks for seamless execution transitions between accelerators while ensuring memory consistency and task dependencies. Although IRIS simplifies system details, optimal dynamic scheduling still requires user input to understand workload structures. To address this, we introduce a new scheduling policy for IRIS, termed IRIS-GNN, which is the first IRIS hybrid policy that operates in conjunction with the dynamic policies. This policy employs a Graph-Neural Network (GNN) to conduct Graph Classification of any task graphs submitted to IRIS. This GNN analyzes the structure and attributes of the task graph, categorizing it as either locality, concurrency, or mixed. This classification subsequently guides the selection of the dynamic policy used by IRIS. We provide a comparison of the performance of IRIS-GNN against the complete spectrum of IRIS's dynamic policies, assess the overhead introduced by the GNN within this scheduling framework, and ultimately explore its practical application in real-world scenarios. © 2024 IEEE.

关键词： accelerators high-performance computing runtime systems scheduling

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Multi-Objective Optimization of high-Speed Permanent Magnet Synchronous Motor Based on Multi-Physical Field Coupling 3

Multi-Objective Optimization of High-Speed Permanent Magnet ...

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3rd International conference on Electronic Information Engineering and computer Science, EIECS 2023

作者： Cui, Jiahang Li, Jianghong Zhao, Zhenmin Shan, Hang Cai, Feichao Northwestern Polytechnical University School of Power and Energy Xi'an China

ISBN: (纸本)9798350316674

high-speed permanent magnet synchronous motors (HSPMSMs) possess distinct attributes, including high frequency, high rotational speed, and high loss density. Thus, in-depth research on each parameter's optimization is crucial to attain maximum performance and reliability. Based on the mechanism of multi-physical field coupling (MPFC), this paper discusses an experimental design method for optimizing a 60,000 rpm, 30 kW motor through multi-objective optimization based on generalized sample space. Special attention is paid to multi-physical field factors such as motor loss, rotor dynamics, and cogging torque. The results demonstrate a considerable improvement in the motor's performance, including its 'goodness' function and cost. Furthermore, the uniform design (UD) method utilizing a genetic algorithm demonstrates high efficiency. Ultimately, the experiment gauging performance and motor temperature rise confirmed a maximum error of 4.2% between simulated and measured results, demonstrating the optimization algorithm's accuracy in multi-physical field coupling scenarios. © 2023 IEEE.

关键词： generalized sample space genetic algorithm high-speed permanent magnet motor (HSPMSM) Multi-physics field coupling (MPFC) uniform design (UD)

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