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6th International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM 2015

作者： Yan, Yonghong Lin, Pei-Hung Liao, Chunhua De Supinski, Bronis R. Quinlan, Daniel J. Department of Computer Science and Engineering Oakland University United States Center of Applied Scientific Computing Lawrence Livermore National Laboratory United States

ISBN: (纸本)9781450334044

Computational accelerators, such as manycore NVIDIA GPUs, Intel Xeon Phi and FPGAs, are becoming common in work-stations, servers and supercomputers for scientific and engineering applications. Efficiently exploiting the massive parallelism these accelerators provide requires the designs and implementations of productive programming models. In this paper, we explore support of multiple accelerators in high-level programming models. We design novel language extensions to OpenMP to support offloading data and computation regions to multiple accelerators (devices). These extensions allow for distributing data and computation among a list of devices via easy-to-use interfaces, including specifying the distribution of multi-dimensional arrays and declaring shared data regions among accelerators. Computation distribution is realized by partitioning a loop iteration space among accelerators. We implement mechanisms to marshal/unmarshal and to move data of non-contiguous array subregions and shared regions between accelerators without involving CPUs. We design reduction techniques that work across multiple accelerators. Combined compiler and runtime support is designed to manage multiple GPUs using asynchronous operations and threading mechanisms. We implement our solutions for NVIDIA GPUs and demonstrate through example OpenMP codes the effectiveness of our solutions for performance improvement. Copyright © 2015 ACM.

关键词： Iterative methods

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Automatic image annotation based on co-training

Automatic image annotation based on co-training

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作者： Ke, Xiao Chen, Guolong College of Mathematics and Computer Science Fuzhou University Fuzhou 350108 China Fujian Key Laboratory of Scientific and Engineering Computing Fuzhou 350108 China

Automatic image annotation is a critical and challenging problem in pattern recognition and image understanding areas. There are some problems in existing automatic image annotation areas. For example, the size of unlabeled data is much larger than the labeled data. Besides, most image annotation models can only use one kind of image segmentation strategy and certain image description method. According to the above problems, an automatic image annotation model based on Co-training is proposed. In this model, four independent feature properties are constructed and then four corresponding sub-classifers are built. In this way, different image segmentation strategies and feature representation methods can be integrated into a unified framework. An adaptive algorithm based on vote and consistency is proposed to extend the training dataset. The proposed method use Co-training algorithm and mass unlabeled data to improve the performance of automatic image annotation. Experiments conducted on Corel 5 K dataset verify the effectiveness of proposed method.

关键词： Image annotation

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An AI Model For Recognization Of Raagas In Indian Classical, Carnatic Music – A Review Article 11

An AI Model For Recognization Of Raagas In Indian Classical,...

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11th IEEE International Conference on Cloud computing in Emerging Markets, CCEM 2023

作者： Govindarajan, Priya Mothi, Shubhada M. Tesfahun, Abebe Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus India Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus India School of Electrical & Computer Engineering Scientific Director Debre Markos Institute of Technology Debre Markos University Ethiopia

ISBN: (纸本)9798350360059

Music is a sound based form of art. From medieval periods there has been a constant effort to create great melodies for the purpose of entertainment, propaganda and healing. There are many formal music systems worldwide that provide a proper method and framework to create great melodies. Music is created by voice and hundreds of musical instruments available worldwide. The two popular classical music systems available in India are Hindustani Classical Music and Carnatic Classical Music. Although there are a few similarities between both the systems such as swaras and Raagas, both have different basic frame work. The basic aim of this review is to collect and analyze all the previous works and research done in the field of Raaga Recognition of Classical Music using Automation and AI. It also involves review of few research journals which are purely based on classification and framework of Carnatic Classical Music in the traditional method without involving Automation or Artificial Intelligence. The analysis is in the pursuit of finding the scope for further research and to create a complete model to recognize maximum possible Raagas with minimal errors. ©2023 IEEE.

关键词： Automation

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Testing the Unknown: A Framework for OpenMP Testing via Random Program Generation

Testing the Unknown: A Framework for OpenMP Testing via Rand...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Laguna, Ignacio Chapman, Patrick Parasyris, Konstantinos Georgakoudis, Giorgis Rubio-Gonzalez, Cindy Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States University of California Department of Computer Science Davis United States

ISBN: (纸本)9798350355543

We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly generate thousands of tests, exposing OpenMP implementations to a wide range of program behaviors. We represent the space of possible random OpenMP tests using a grammar and implement our method as an extension of the Varity program generator. By generating 1,800 OpenMP tests, we find various performance anomalies and correctness issues when we apply them to three OpenMP implementations: GCC, Clang, and Intel. We also present several case studies that analyze the anomalies and give more details about the classes of tests that our approach creates. © 2024 IEEE.

关键词： differential testing OpenMP random program generation software testing

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Partitioned fluid-structure-acoustics interaction on distributed data: Coupling via preCICE

Partitioned fluid-structure-acoustics interaction on distrib...

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International Conference on Software for Exascale computing, SPPEXA 2015

作者： Bungartz, Hans-Joachim Lindner, Florian Mehl, Miriam Scheufele, Klaudius Shukaev, Alexander Uekermann, Benjamin Scientific Computing in Computer Science Technical University of Munich München Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany

ISBN: (纸本)9783319405261

One of the great prospects of exascale computing is to simulate challenging highly complex multi-physics scenarios with different length and time scales. A modular approach re-using existing software for the single-physics model parts has great advantages regarding flexibility and software development costs. At the same time, it poses challenges in terms of numerical stability and parallel scalability. The coupling library preCICE provides communication, data mapping, and coupling numerics for surface-coupled multi-physics applications in a highly modular *** recapitulate the numerical methods but focus particularly on their parallel implementation. The numerical results for an artificial coupling interface show a very small runtime of the coupling compared to typical solver runtimes and a good parallel scalability on a number of cores corresponding to a massively parallel simulation for an actual, coupled simulation. Further results for actual application scenarios from the field of fluid-structure-acoustic interactions are presented in the next chapter. © Springer International Publishing Switzerland 2016.

关键词： Numerical methods

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Toward a Unified Hybrid HPCQC Toolchain 4

Toward a Unified Hybrid HPCQC Toolchain

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4th IEEE International Conference on Quantum computing and Engineering, QCE 2023

作者： Seitz, Philipp Elsharkawy, Amr To, Xiao-Ting Michelle Schulz, Martin Technical University of Munich Scientific Computing in Computer Science Munich Germany Ludwig-Maximilians-Universität in Munich Mnm Team Munich Germany

ISBN: (纸本)9798350343236

In the expanding field of quantum computing (QC), efficient and seamless integration of QC and high performance computing (HPC) elements (e.g., quantum hardware, classical hardware, and software infrastructure on both sides) plays a crucial role. This paper addresses the development of a unified toolchain designed for hybrid quantum-classical systems. Our work proposes a design for a unified hybrid high performance computing - quantum computing (HPCQC) toolchain that tackles pressing issues such as scalability, cross-technology execution, and ahead-of-time (AOT) optimization. © 2023 IEEE.

关键词： Qubits

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A multilevel algorithm to compute steady states of lattice boltzmann models

A multilevel algorithm to compute steady states of lattice b...

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International Research Workshop: Coping with Complexity: Model Reduction and Data Analysis

作者： Samaey, Giovanni Vandekerckhove, Christophe Vanroose, Wim Scientific Computing Department of Computer Science K.U. Leuven Celestijnenlaan 200A Leuven 3001 Belgium Department of Mathematics and Computer Science Universiteit Antwerpen Middelheimlaan 1 Antwerpen 2020 Belgium

ISBN: (纸本)9783642149405

We present a multilevel algorithm to compute steady states oflattice Boltzmann models directly as fixed points of a time-stepper. At the fine scale, we use a Richardson iteration for the fixed point equation, which amounts to time-stepping towards equilibrium. This fine-scale iteration is accelerated by transferring the error to a coarse level. At this coarse level, one directly solves for the density (the zeroth moment of the lattice Boltzmann distributions), for which a coarse-level equation is known in some appropriate limit. The algorithm closely resembles the classical multigrid algorithm in spirit, structure and convergence behaviour. In this paper, we discuss the formulation of this algorithm. We give an intuitive explanation of its convergence behaviour and illustrate with numerical experiments.

关键词： Boltzmann equation

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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On the Scalability of computing Genomic Diversity Using SparkLeBLAST: A Feasibility Study

On the Scalability of Computing Genomic Diversity Using Spar...

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2024 IEEE High Performance Extreme computing Conference, HPEC 2024

作者： Prabhu, Ritvik Moussad, Bernard Youssef, Karim Vatai, Emil Feng, Wu-Chun Virginia Tech Department of Computer Science United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Riken Center for Computational Science Japan

ISBN: (纸本)9798350387131

Studying the genomic diversity of viruses can help us understand how viruses evolve and how that evolution can impact human health. Rather than use a laborious and tedious wet-lab approach to conduct a genomic diversity study, we take a computational approach, using the ubiquitous NCBI BLAST and our parallel and distributed SparkLeBLAST, across 53 patients (40,000,000 query sequences) on Fugaku, the world's fastest homogeneous supercomputer with 158,976 nodes, where each code contains a 48-core A64FX processor and 32 GB RAM. To project how long BLAST and SparkLeBLAST would take to complete a genomic diversity study of COVID-19, we first perform a feasibility study on a subset of 50 query sequences from a single COVID-19 patient to identify bottlenecks in sequence alignment processing. We then create a model using Amdahl's law to project the run times of NCBI BLAST and SparkLeBLAST on supercomputing systems like Fugaku. Based on the data from this 50-sequence feasibility study, our model predicts that NCBI BLAST, when running on all the cores of the Fugaku supercomputer, would take approximately 26.7 years to complete the full-scale study. In contrast, SparkLeBLAST, using both our query and database segmentation, would reduce the execution time to 0.026 years (i.e., 22.9 hours) - resulting in more than a 10,000× speedup over using the ubiquitous NCBI BLAST. © 2024 IEEE.

关键词： COVID-19

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Ultra-fast NoC emulation on a single FPGA 25

Ultra-fast NoC emulation on a single FPGA

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25th International Conference on Field Programmable Logic and Applications, FPL 2015

作者： Van Chu, Thiem Sato, Shimpei Kise, Kenji Dept. of Computer Science Tokyo Institute of Technology Tokyo Japan Global Scientific Information and Computing Center Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9780993428005

Network-on-Chip (NoC) has become the de facto on-chip communication architecture for many-core systems. This paper proposes novel methods for emulating large-scale NoC designs on a single FPGA. Since FPGAS offer a highly parallel platform, FPGA-based emulation can be much faster than the software-based approach. However, emulating NoC designs with up to thousands of nodes is a challenging task due to the FPGA capacity constraints. We first describe how to accurately model synthetic workloads on FPGA by separating the time of the emulated network and the times of the traffic generation units. We next present a novel use of time-multiplexing in emulating the entire network using several physical nodes. Finally, we show the basic steps to apply the proposed methods to emulate different NoC architectures. The proposed methods enable ultrafast emulations of large-scale NoC designs with up to thousands of nodes using only on-chip resources of a single FPGA. In particular, more than 5,000× simulation speedup over BookSim, a widely used software-based NoC simulator, is achieved. © 2015 Imperial College.

关键词： Field programmable gate arrays (FPGA)

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