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A novel application development environment for large-scale scientific computations 00

A novel application development environment for large-scale ...

引用

Proceedings of the 14th international conference on Supercomputing

作者： X. Shen W. Liao A. Choudhary G. Memik M. Kandemir S. More G. Thiruvathukal A. Singh Center for Parallel and Distributed Computing Department of Electrical and Computer Engineering Northwestern University Evanston IL Department of Computer Science and Engineering The Pennsylvania State University University Park PA School of Computing Telecommunications and Information Sciences JHPC Laboratory Depaul University

Our results demonstrate that our novel application development environment provides both ease-of-use and high performance for large-scale, I/O-intensive scientific applications.

ISBN: (纸本)9781581132700

Our results demonstrate that our novel application development environment provides both ease-of-use and high performance for large-scale, I/O-intensive scientific applications.

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An enhanced parallel loop self-scheduling scheme for cluster environments

An enhanced parallel loop self-scheduling scheme for cluster...

引用

International Conference on Advanced Information Networking and Applications (AINA)

作者： Chao-Tung Yang Kuan-Wei Cheng Kuan-Ching Li High-Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung Taiwan

ISBN: (纸本)0769522491

In this paper, a parallel loop self-scheduling scheme for heterogeneous PC cluster systems is proposed. Though the proposed scheme does allow users to choose parameters before the execution initialization phase, there are still weaknesses that motivate us to go further with new improvements in that scheme. For instance, a decision on a fixed and monotonous parameter can easily lead to invalid schedule by using previous input information. Thus, it is proposed in this paper a new scheme, where the scheduling parameter can be adjusted dynamically and fit into most widely available computer systems, in order to provide higher overall performance.

关键词： Processor scheduling Dynamic scheduling Load management Master-slave Concurrent computing distributed computing Clustering algorithms Runtime Computer science Scheduling algorithm

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On implementation of an enhanced and integrated parallel programming Web-based toolkit

On implementation of an enhanced and integrated parallel pro...

引用

International Conference on Information Technology: Research and Education (ITRE)

作者： Kuan-Ching Li Chao-Tung Yang Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung Taiwan High Performance Computing Laboratory Department of Computer Science and Infnmation Engineering Tunghai University Taichung Taiwan

ISBN: (纸本)0780389328

The computing power provided by high performance and low cost PC-based clusters and grid computing platforms are attractive and they are equal or superior to supercomputers and mainframes. In parallel, discussions on how to obtain more computing power from these computing platforms become an interesting issue. The development of applications for these high-performance computing platforms is complicated for several reasons: the complexity of applications themselves, which combines aspects of super computing and distributed computing, and by the need to achieve higher performance. This paper describes the design rationale and implementation of a parallel programming Web-based toolkit, to ease the parallel programming learning process, with the use of Web-based interface. The toolkit has widely been used in MPI parallel programming courses (both in graduate and undergraduate levels) and industry trainings.

关键词： parallel programming Concurrent computing Application software distributed computing Grid computing High performance computing Visualization Computer science Supercomputers Industrial training

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RAFlow: Read Ahead Accelerated I/O Flow through Multiple Virtual Layers

RAFlow: Read Ahead Accelerated I/O Flow through Multiple Vir...

引用

International Conference on Networking, Architecture, and Storage (NAS)

作者： Zhaoning Zhang Kui Wu Huiba Li Jinghua Feng Yuxing Peng Xicheng Lu National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China Computer Science Department University of Victoria B.C. Canada National Super Computing Center Tianjin China

Virtualization is the foundation for cloud computing, and the virtualization can not be achieved without software defined, elastic, flexible and scalable virtual layers. Unfortunately, if multiple virtual storage devices are chained together, the system may be subject to severe performance degradation. While the read-ahead (RA) mechanism in storage devices plays a very important role to improve I/O performance, RA may not be effective as expected for multiple virtualization layers, since it is originally designed for one layer only. When I/O requests are passed through a long I/O path, they may trigger a chain reaction and lead to unnecessary data transmission and thus bandwidth waste. In this paper, we study the dynamic behavior of RA through multiple I/O layers and demonstrate that if controlled well, RA can greatly accelerate I/O speed. We present RAFlow, a RA control mechanism, to effectively improve I/O performance by strategically expanding RA window at each layer. Our real-world experiments show that it can achieve 20% to 50% performance improvement in I/O paths with up to 8 virtualized storage devices.

关键词： Radio access networks Acceleration distributed databases Performance evaluation Synchronization Benchmark testing Degradation

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Decision tree construction for data mining on grid computing environments

Decision tree construction for data mining on grid computing...

引用

International Conference on Advanced Information Networking and Applications (AINA)

作者： Chao-Tung Yang Shu-Tzu Tsai Kuan-Ching Li High-Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung Taiwan

ISBN: (纸本)0769522491

In this paper, the authors presented the grid-based decision tree architecture, with the intention of applying it to both parallel and sequential algorithms. Also, it is shown that, based on the scope and model of data mining applied in the grid environment as well as user equivalent perspective, grid roles can be categorized into three types. It was aimed, through these definitions, to help software developers define clear system processes and differentiate the application scope for software applications. To fulfill the architecture, an existing parallel decision tree algorithm was first applied (the SPRINT algorithm) to the grid environment. The performance and differences in many other areas are compared using datasets of different sizes. The experimental results will be used for future reference and further development.

关键词： Decision trees Data mining Grid computing Computer architecture Clustering algorithms Concurrent computing distributed computing Computer science Application software Chaos

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The anatomy of a course in cluster and grid computing

The anatomy of a course in cluster and grid computing

引用

International Conference on Information Technology: Research and Education (ITRE)

作者： Chao-Tung Yang Kuan-Ching Li High Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung Taiwan

ISBN: (纸本)0780389328

Cluster and grid computing is a relatively new interdisciplinary field, where computer science, engineering and computational biology are its core supporting disciplines. The rise of cluster and grid computing discipline brings to computer science faculty members new opportunities and challenges, both in education and in research. In this paper, we explore issues in teaching cluster and grid computing, the developing of curricula, and thoughts to foster student to work on research projects in cluster and grid computing.

关键词： Anatomy Grid computing distributed computing Concurrent computing Computer networks Brain modeling Predictive models Computer science Education Space technology

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Constrained Projective Non-negative Matrix Factorization for Semi-supervised Multi-label Learning

Constrained Projective Non-negative Matrix Factorization for...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Xiang Zhang Naiyang Guan Zhigang Luo Xuejun Yang College of Computer National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China The State key Laboratory of High Performance Computing National University of Defense Technology Changsha China

This paper formulates multi-label learning as a constrained projective non-negative matrix factorization (CPNMF) problem which concentrates on a variant of the original projective NMF (PNMF) and explicitly introduces an auxiliary basis to learn the semantic subspace and boosts its discriminating ability by exploiting labeled and unlabeled examples together. Particularly, it propagates labels of the labeled examples to the unlabeled ones by enforcing coefficients of examples sharing identical semantic contents to be identical based on a hard constraint, i.e., embedding the class indicator of labeled examples into their coefficients. CPNMF preserves the geometrical structure of dataset via manifold regularization meanwhile captures the inherent structure of labels by using label correlations. We developed a multiplicative update rule (MUR) based algorithm to optimize CPNMF and proved its convergence. Experiments of image annotation on Corel dataset, text categorization on Rcv1v2 dataset, and text clustering on two popular text corpuses suggest the effectiveness of CPNMF.

关键词： Conferences

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Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning 4

Simulation of Nuclear Reactor Accident Scenarios using Physi...

引用

4th International Conference on Electronic Information Engineering and Computer Science, EIECS 2024

作者： Yu, Yang Xie, Yufei Wang, Wenlin Wu, Guohua Lan, Haitao Lin, Enbo An, Ping Sun, Zibin Zhang, Haichuan Wu, Yixian National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (纸本)9798331531409

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R2: 0.735). © 2024 IEEE.

关键词： Coolants

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parallel biomedical image processing with GPGPUs in cancer research

Parallel biomedical image processing with GPGPUs in cancer r...

引用

International Symposium on Logistics and Industrial Informatics, LINDI

作者： Attila Reményi Sándor Szénási István Bándi Zoltán Vámossy Gábor Valcz Pál Bogdanov Szabolcs Sergyán Miklos Kozlovszky John von Neumann Faculty of Informatics Óbuda University Budapest Hungary 2nd Department of Internal Medicine Semmelweiss University Budapest Hungary Laboratory of Parallel and Distributed Computing MTA SZTAKI Budapest Hungary

The main aim of this work is to show, how GPGPUs can facilitate certain type of image processing methods. The software used in this paper is used to detect special tissue part, the nuclei on (HE - hematoxilin eosin) stained colon tissue sample images. Since pathologists are working with large number of high resolution images - thus require significant storage space -, one feasible way to achieve reasonable processing time is the usage of GPGPUs. The CUDA software development kit was used to develop processing algorithms to NVIDIA type GPUs. Our work focuses on how to achieve better performance with coalesced global memory access when working with three-channel RGB tissue images, and how to use the on-die shared memory efficiently.

关键词： Graphics processing unit Instruction sets Image processing Memory management Optimization Kernel Colon

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Optimizing Batched Small Matrix Multiplication on Multi-core DSP Architecture

Optimizing Batched Small Matrix Multiplication on Multi-core...

引用

International Symposium on parallel and distributed Processing with Applications, ISPA

作者： Xiaohan Zuo Chunhua Xiao Qinglin Wang Chen Shi College of Computer Science Chongqing University China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many High-Performance computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x.

关键词： Performance evaluation Scientific computing High performance computing Digital signal processors Redundancy Batch production systems Signal processing algorithms Computer architecture Machine learning Matrix decomposition

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