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Proceedings of the Advances in Parallel and Distributed computing

作者： Xinda Lu Yi Hu Jie Chen High Performance Computing Lab Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

ISBN: (纸本)0818678763

This paper presents a lifetime-sensitive scheduling method. By shortening lifetimes of variables in scheduling phase, it can lighten register pressure in register allocation phase, lessen spill codes and result in more efficient object codes. The preliminary experimental results show that this method is an effective scheduling method.

关键词： Costs Processor scheduling Optimizing compilers Registers Parallel processing high performance computing computer science Reduced instruction set computing Upper bound performance evaluation

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Implementing the matrix multiplication with dfc on kunlun small scale computer 20

Implementing the matrix multiplication with dfc on kunlun sm...

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20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Du, Zheng Zhang, Jing Sha, Shihao Luo, Qiuming Department of Computer Science and Technology Tsinghua University China NHPCC/Guangdong Key Laboratory of Popular HPC Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781728126166

In this paper, we demonstrate a new dataflow platform of DFC, which can handle the successive dataflow computing passes with tagged data. By implementing the matrix multiplication in DFC, we show that DFC can exploit the parallelism automatically with a much simple dataflow graph constructed by DF functions of DFC. Different from the other dataflow execution platform, DFC support multiple worker threads for one dataflow node of DF functions. By running the matrix multiplication program of DFC on Kunlun system, it was verified that DFC get a reasonable speedup for large scale computing for thread number up to 512. © 2019 IEEE.

关键词： Matrix algebra

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An energy-efficient virtual machine scheduler with I/O collective mechanism in resource virtualisation environments

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International Journal of Networking and Virtual Organisations 2013年第4期13卷 311-326页

作者： Xiao, Peng Liu, Dongbo Department of Computer Science High-Performance Computing Center Hunan Institute of Engineering Hunan Province China

Recently, resource virtualisation has been proven effective for deploying large-scale IT-infrastructures, such as grids and clouds. However, many studies also indicate that the system's energy-efficiency will be reduced when I/O virtualisation is involved. In this paper, we present an energy-efficiency enhanced virtual machine (VM) scheduler with aiming at reducing the energy-efficiency losses caused by I/O virtualisation. The proposed VM scheduler is incorporated with an I/O collective mechanism, which separates I/O-intensive VMs from CPU-intensive ones during the runtime and schedules them in a batch manner, so as to reduce the context-switching costs when scheduling intensive mixed workloads. Extensive experiments are conducted on various platforms by using different benchmarks to investigate the performance of the proposed policy. The experimental results indicate that when the virtualisation platform is in presence of mixed workloads, the proposed scheduler outperforms many existing VM schedulers in term of energy-efficiency. Copyright © 2013 Inderscience Enterprises Ltd.

关键词： Virtual machine

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Generative Methods for Out-of-distribution Prediction and Applications for Threat Detection and Analysis: A Short Review 1st

Generative Methods for Out-of-distribution Prediction and Ap...

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1st International Workshop on Digital Sovereignty in Cyber Security: New Challenges in Future Vision, CyberSec4Europe 2022

作者： Coppolillo, Erica Liguori, Angelica Guarascio, Massimo Pisani, Francesco Sergio Manco, Giuseppe Department of Mathematics and Computer Science University of Calabria Rende Italy Department of Computer Engineering Modeling Electronics an d Systems University of Calabria Rende Italy Institute for High Performance Computing and Networking of Italian National Research Council Rende Italy

ISBN: (纸本)9783031360954

In recent times, Machine Learning has played an important role in developing novel advanced tools for threat detection and mitigation. Intrusion Detection, Misinformation, Malware, and Fraud Detection are just some examples of cybersecurity fields in which Machine Learning techniques are used to reveal the presence of malicious behaviors. However, Out-of-Distribution, i.e., the potential distribution gap between training and test set, can heavily affect the performances of the traditional Machine Learning based methods. Indeed, they could fail in identifying out-of-samples as possible threats, therefore devising robust approaches to cope with this issue is a crucial and relevant challenge to mitigate the risk of undetected attacks. Moreover, a recent emerging line proposes to use generative models to yield synthetic likely examples to feed the learning algorithms. In this work, we first survey recent Machine Learning and Deep Learning based solutions to face both the problems, i.e., outlier detection and generation;then we illustrate the main cybersecurity application scenarios in which these approaches have been adopted successfully. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Learning systems

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A framework for faster porting of scientific applications between heterogeneous clouds 1st

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1st International Conference on Infrastructure, Technologies and Applications, SCITA 2017

作者： Ahmed, Waseem Khan, Mohsin Khan, Adeel Ahmed Mehmood, Rashid Algarni, Abdullah Albeshri, Aiiad Katib, Iyad Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India High Performance Computing Center King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9783319941790

The emergence of pay-as-you-use compute clouds has enabled scientists to experiment with the latest processor architectures and accelerators. However, the lack of standardization in cloud computing, more specifically in the interoperability context, makes the task of portability of applications between clouds challenging. Two main tasks that users of multi-vendor clouds will need to perform are porting cost analysis and faster source-to-source translation. Cost analysis is essential to help evaluate the feasibility and cost of portability. And any automation of the source-to-source translation step will help developers perform the translation faster while taking advantage of platform-specific features. This paper presents a framework that assists a developer in performing these two tasks. The first task is achieved using the Maintainability Analyzer module which generates unique funnel shaped patterns that give an insight about the maintainability of an application and its potential for porting. Different scientific applications from various domains, that were developed using different programming paradigms, were evaluated using this module. For the second task, a set of modules use a knowledge repository to perform source-to-source translations while ensuring the maintainability of the generated code. The framework has been tested with different architecture and library combinations with promising results. © 2018, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Cost benefit analysis

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Evaluating the flexibility of dynamic loop scheduling on heterogeneous systems in the presence of fluctuating load using SimGrid

Evaluating the flexibility of dynamic loop scheduling on het...

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2013 IEEE 37th Annual computer Software and Applications Conference, COMPSAC 2013

作者： Sukhija, Nitin Banicescu, Ioana Srivastava, Srishti Ciorba, Florina M. Mississippi State University Department of Computer Science and Engineering Mississippi State MS 39762 United States Technische Universiẗat Dresden Center for Information Services and High Performance Computing 01062 Dresden Germany

ISBN: (纸本)9780769549798

Scientific applications running on heterogeneous computing systems, which often have unpredictable behavior, enhance their performance by employing loop scheduling techniques as methods to avoid load imbalance through an optimized assignment of their parallel loops. With current computing platforms facilitating petascale performance and promising exascale performance towards the end of the present decade, efficient and robust algorithms are required to guarantee optimal performance of parallel applications in the presence of unpredictable perturbations. A number of dynamic loop scheduling (DLS) methods based on probabilistic analyses have been developed to achieve the desired robust performance. In earlier work, two metrics (flexibility and resilience) have been formulated to quantify the robustness of various DLS methods in heterogeneous computing systems with uncertainties. In this work, to ensure robust performance of the scientific applications on current (petascale) and future(exascale) high performance computing systems, a simulation model was designed and integrated into the SimGrid simulation toolkit, thus enabling a comprehensive study of the robustness of the DLS methods which uses results of experimental cases with various combinations of number of processors, problem sizes, and scheduling methods. The DLS methods have been implemented into the simulation model and analyzed for the purpose of exploring their flexibility (robustness against unpredictable variations in the system load), when involved in a range of case scenarios comprised of various distributions characterizing loop iteration execution times and system availability. The simulation results reported are used to compare the robustness of the DLS methods under the various environments considered, using the flexibility metric. © 2013 IEEE.

关键词： Iterative methods

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Online Demand Peak Shaving with Machine-Learned Advice in Cyber-Physical Energy Systems

Online Demand Peak Shaving with Machine-Learned Advice in Cy...

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2023 IEEE International Conference on Dependable, Autonomic and Secure computing, 2023 International Conference on Pervasive Intelligence and computing, 2023 International Conference on Cloud and Big Data computing, 2023 International Conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Fengl, Minxi Li, Wei Qin, Boyu Zomaya, Albert Y. Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Xi'an Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipment Department of Electrical Engineering China

ISBN: (纸本)9798350304602

With the deep integration of cyber tools, control algorithms are increasingly employed in cyber-physical energy systems to enhance management, cost efficiency, and robustness. Effective demand load management is crucial in cyber-physical energy systems as it directly impacts operational costs. Failure to adequately manage spiky or seasonal demand loads can lead to significant expenses on monthly utility bills. In this study, we propose AMPAMOD, a randomized online algorithm with machine-learned advice, to optimize battery operations and mitigate highly dynamic peak loads. AMPAMOD utilizes limited advice from machine learning algorithms to guide our online algorithm and ensure cost-effective peak load management. The theoretical analysis shows that our solution has minimal advice complexity, a linear computational cost, and an improved competitive ratio. Finally, we conduct extensive trace-driven experiments on real-world datasets. AMPAMOD achieves a peak shaving success rate of over 90%, outperforming baselines by at least 50%. These experimental results confirm theoretical findings and demonstrate the potential of AMPAMOD. © 2023 IEEE.

关键词： Cyber Physical System

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Vision Transformers with Hierarchical Attention

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Machine Intelligence Research 2024年第4期21卷 670-683页

作者： Yun Liu Yu-Huan Wu Guolei Sun Le Zhang Ajad Chhatkuli Luc Van Gool Institute for Infocomm Research(I2R) A*STARSingapore 138632Singapore Institute of High Performance Computing(IHPC) A*STARSingapore 138632Singapore Computer Vision Lab ETH ZürichZürich 8092Switzerland School of Information and Communication Engineering University of Electronic Science and Technology of China(UESTC)Chengdu 611731China

This paper tackles the high computational/space complexity associated with multi-head self-attention(MHSA)in vanilla vision *** this end,we propose hierarchical MHSA(H-MHSA),a novel approach that computes self-attention in a hierarchical ***,we first divide the input image into patches as commonly done,and each patch is viewed as a ***,the proposed H-MHSA learns token relationships within local patches,serving as local relationship ***,the small patches are merged into larger ones,and H-MHSA models the global dependencies for the small number of the merged *** last,the local and global attentive features are aggregated to obtain features with powerful representation *** we only calculate attention for a limited number of tokens at each step,the computational load is reduced ***,H-MHSA can efficiently model global relationships among tokens without sacrificing fine-grained *** the H-MHSA module incorporated,we build a family of hierarchical-attention-based transformer networks,namely *** demonstrate the superiority of HAT-Net in scene understanding,we conduct extensive experiments on fundamental vision tasks,including image classification,semantic segmentation,object detection and instance ***,HAT-Net provides a new perspective for vision *** and pretrained models are available at https://***/yun-liu/HAT-Net.

关键词： Vision transformer hierarchical attention global attention local attention scene understanding.

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An efficient technique for optimization of the Concentric Array Radial Line Slot Antenna (CA-RLSA)

An efficient technique for optimization of the Concentric Ar...

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2005 IEEE Antennas and Propagation Society International Symposium and USNC/URSI Meeting

作者： Feng, Zhuo Li, Le-Wei Li, Er-Ping Yeo, Tat Soon Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 119260 Singapore Computational Electronics and Electromagnetics Division Institute of High Performance Computing Science Park II Singapore 117528 Singapore

ISBN: (纸本)0780388836

In this paper, an efficient technique is proposed to optimize the geometie and arrangements of the slots of the CARLSAs. Moment method in carried out to obtain the unknown excitation coefficients after spliting the slot pairs into several identical sectors. The slot lengths and positions are then optimized to achieve the uniform amplitude and phase over the aperture. The computation of the admittance matrix is reduced a lot while the unknowns of the slot excitation coefficients are also minimized. © 2005 IEEE.

关键词： Antenna arrays

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Predicting the flexibility of dynamic loop scheduling using an artificial neural network

Predicting the flexibility of dynamic loop scheduling using ...

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2013 IEEE 12th International Symposium on Parallel and Distributed computing, ISPDC 2013

作者： Srivastava, Srishti Malone, Brandon Sukhija, Nitin Banicescu, Ioana Ciorba, Florina M. Department of Computer Science and Engineering Mississippi State University MS United States Department of Computer Science Helsinki Institute for Information Technology University of Helsinki Helsinki Finland Center for Information Services and High Performance Computing Technische Universität Dresden Dresden Germany

ISBN: (纸本)9780769550183

In this paper, an artificial neural network (ANN) model is proposed to predict the flexibility (or robustness against system load fluctuations in heterogeneous computing systems) of dynamic loop scheduling (DLS) methods. The multilayer perceptron (MLP) ANN model has been used to predict the degree of robustness of a DLS method, given specific values for the problem size, the system size, and the characteristics of the system load fluctuations as a compound effect of the variations in the application's iteration execution times and the processor availabilities. The developed MLP ANN model can be useful in an effective selection of the most robust DLS technique for scheduling a certain type of scientific application onto a given set of non-dedicated heterogeneous processors, when their system load is expected to fluctuate unpredictably during the application's runtime. © 2013 IEEE.

关键词： Forecasting

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