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作者： Chen, Zhenbang Hannousse, Abdel Hakim Van Hung, Dang Knoll, Istvan Li, Xiaoshan Liu, Zhiming Liu, Yang Nan, Qu Okika, Joseph C. Ravn, Anders P. Stolz, Volker Yang, Lu Zhan, Naijun International Institute for Software Technology United Nations University China Faculty of Science and Technology University of Macau Department of Computer Science Aalborg University Denmark Lab. of Computer Science Institute of Software CAS China National Laboratory for Parallel and Distributed Processing China

ISBN: (纸本)3540852883

This chapter presents a formalization of functional and behavioural requirements, and a refinement of requirements to a design for CoCoME using the Relational Calculus of Object and Component Systems (rCOS). We give a model of requirements based on an abstraction of the use cases described in Chapter 3.2. Then the refinement calculus of rCOS is used to derive design models corresponding to the top level designs of Chapter 3.4. We demonstrate how rCOS supports modelling different views and their relationships of the system and the separation of concerns in the development. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Calculations

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TeeSwap: Private Data Exchange using Smart Contract and Trusted Execution Environment 23

TeeSwap: Private Data Exchange using Smart Contract and Trus...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Peng Shi, Peichang Xu, Jie Fu, Xiang Li, Linhui Zhong, Tao Xiang, Liangliang Kong, Jinzhu College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China Zhejiang Lab Hangzhou311100 China Kylin Software Co. Ltd Changsha410073 China

ISBN: (纸本)9781665494571

With importance of data value is approved, data-sharing will create more and greater value has become consensus. However, data exchange has to use a trusted third party(TTP) as an intermediary in an untrusted network environment, which may lead to leakage of data privacy. In this paper, we design a system named TeeSwap, which uses smart contract to complete data exchange, and at the same time uses cryptographic algorithms and trusted execution environment (TEE) to ensure privacy in process of data transmission, verification and contract execution. Meanwhile, TeeSwap can ensure the credibility, fairness, integrity and privacy while data are shared in our system. In the end, we implement contracts about data market and data exchange. When confirming the data transaction, data market contract can automatically generate and deploy exchange contract, which can complete the data exchange authoritatively. © 2021 IEEE.

关键词： Data privacy Smart contracts Data communication Cryptography

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Learning Non-local Image Diffusion for Image Denoising 17

Learning Non-local Image Diffusion for Image Denoising

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25th ACM International Conference on Multimedia (MM)

作者： Qiao, Peng Dou, Yong Feng, Wensen Li, Rongchun Chen, Yunjin Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China ULSee Inc Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450349062

Image diffusion plays a fundamental role for the task of image denoising. The recently proposed trainable nonlinear reaction diffusion (TNRD) model defines a simple but very effective framework for image denoising. However, as the TNRD model is a local model, whose diffusion behavior is purely controlled by information of local patches, it is prone to create artifacts in the homogenous regions and over-smooth highly textured regions, especially in the case of strong noise levels. Meanwhile, it is widely known that the non-local self-similarity (NSS) prior stands as an effective image prior for image denoising, which has been widely exploited in many non-local methods. In this work, we are highly motivated to embed the NSS prior into the TNRD model to tackle its weaknesses. In order to preserve the expected property that end-to-end training remains available, we exploit the NSS prior by defining a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising. Together with the local filters and influence functions, the non-local filters are learned by employing loss-specific training. The experimental results show that the trained TNLRD model produces visually plausible recovered images with more textures and less artifacts, compared to its local versions. Moreover, the trained TNLRD model can achieve strongly competitive performance to recent state-of-the-art image denoising methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).

关键词： Image denoising non-local self-similarity reaction diffusion loss-based training

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Performance Analysis of Multimedia Retrieval Workloads Running on Multicores

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2016年第11期27卷 3323-3337页

作者： Lu, Yunping Wang, Xin Zhang, Weihua Chen, Haibo Peng, Lu Zhao, Wenyun Fudan Univ Shanghai Key Lab Data Sci Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Parallel Proc Inst Shanghai Peoples R China Fudan Univ Software Sch Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Louisiana State Univ Div Elect & Comp Engn Baton Rouge LA 70803 USA

Multimedia data has become a major data type in the Big Data era. The explosive volume of such data and the increasing real-time requirement to retrieve useful information from it have put significant pressure in processing such data in a timely fashion. However, while prior efforts have done in-depth analysis on architectural characteristics of traditional multimedia processing and text-based retrieval algorithms, there has been no systematic study towards the emerging multimedia retrieval applications. This may impede the architecture design and system evaluation of these applications. In this paper, we make the first attempt to construct a multimedia retrieval benchmark suite (MMRBench for short) that can be used to evaluate architectures and system designs for multimedia retrieval applications. MMRBench covers modern multimedia retrieval algorithms with different versions (sequential, parallel and distributed). MMRBench also provides a series of flexible interfaces as well as certain automation tools. With such a flexible design, the algorithms in MMRBench can be used both in individual kernel-level evaluation and in integration to form a complete multimedia data retrieval infrastructure for full system evaluation. Furthermore, we use performance counters to analyze a set of architecture characteristics of multimedia retrieval algorithms in MMRBench, including the characteristics of core level, chip level and inter-chip level. The study shows that micro-architecture design in current processor is inefficient (both in performance and power) for these multimedia retrieval workloads, especially in core resources and memory systems. We then derive some insights into the architecture design and system evaluation for such multimedia retrieval algorithms.

关键词： Multimedia retrieval benchmarks architectural characteristics

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PANGU-Σ: TOWARDS TRILLION PARAMETER LANGUAGE MODEL WITH SPARSE HETEROGENEOUS COMPUTING

arXiv

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arXiv 2023年

作者： Ren, Xiaozhe Zhou, Pingyi Meng, Xinfan Huang, Xinjing Wang, Yadao Wang, Weichao Li, Pengfei Zhang, Xiaoda Podolskiy, Alexander Arshinov, Grigory Bout, Andrey Piontkovskaya, Irina Wei, Jiansheng Jiang, Xin Su, Teng Liu, Qun Yao, Jun Noah’s Ark Lab Huawei Technologies China Distributed and Parallel Software Lab Huawei Technologies China

The scaling of large language models has greatly improved natural language understanding, generation, and reasoning. In this work, we develop a system that trained a trillion-parameter language model on a cluster of Ascend 910 AI processors 2 and MindSpore framework 3, and present the language model with 1.085T parameters named PanGu-Σ. With parameter inherent from PanGu-α [1], we extend the dense Transformer model to sparse one with Random Routed Experts (RRE), and efficiently train the model over 329B tokens by using Expert Computation and Storage Separation (ECSS). This resulted in a 6.3x increase in training throughput through heterogeneous computing. Our experimental findings show that PanGu-Σ provides state-of-the-art performance in zero-shot learning of various Chinese NLP downstream tasks. Moreover, it demonstrates strong abilities when fine-tuned in application data of open-domain dialogue, question answering, machine translation and code generation. Copyright © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

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A direct execution approach to simulating mobile agent algorithms

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JOURNAL OF SUPERCOMPUTING 2004年第2期29卷 171-184页

作者： Li, XH Cao, JN He, YX Hong Kong Polytech Univ Dept Comp Internet & Mobile Comp Lab Kowloon Hong Kong Peoples R China Wuhan Univ State Key Lab Software Engn Data & Knowledge Engn Lab Wuhan 430072 Hubei Peoples R China Wuhan Univ Sch Comp Parallel & Distributed Comp Lab Wuhan 430072 Hubei Peoples R China

Mobile agent technology has been applied to develop the solutions for various kinds of parallel and distributed computing problems. However, performance evaluation of mobile agent algorithms remains a difficult task, mainly due to the characteristics of mobile agents such as distributed and asynchronous execution, autonomy and mobility. This paper proposes a general approach based on direct execution simulation for evaluating the performance of mobile agent algorithms by collecting and analyzing the information about the agents during their execution. We describe the proposed generic simulation model, named MADES, the architecture of a software environment based on MADES, and a prototype implementation. A mobile agent-based distributed load balancing algorithm has been used for experiments with the prototype.

关键词： mobile agent direct execution simulation parallel and distributed simulation MADES

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A mesh quality discrimination method based on convolutional neural network

A mesh quality discrimination method based on convolutional ...

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2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020

作者： Xu, Zhenpeng Chen, Xinhai Chi, Lihua Liu, Jie Gong, Chunye Sci. and Technol. on Parallel and Distributed Proc. Lab. Lab. of Software Eng. for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728170046

With the rapid development of highperformance computing, computational fluid dynamics (CFD) has become an important part of hydrodynamics and aerodynamics. Mesh quality is the key factor that affects the accuracy and efficiency of CFD numerical calculation. However, the current the process of mesh quality discrimination is very time-consuming. The manpower time needed for this process takes up a large proportion in the whole numerical calculation process. A large number of artificial intelligence algorithms have been put forward to replace the human to efficiently complete all kinds of tedious tasks. In this paper, we propose a convolutional neural network (CNN) based mesh quality discrimination method, MeshNet. MeshNet uses residual neural network structure to learn mesh features and automatically judge the mesh quality. The experimental results show that the proposed network can greatly save labor time cost and achieve an accuracy of 94.41% for mesh quality discrimination. © 2020 IEEE.

关键词： Convolutional neural networks

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YES SIR!Optimizing semantic space of Negatives with Self-Involvement Ranker

arXiv

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arXiv 2021年

作者： Pu, Ruizhi Zhang, Xinyu Lai, Ruofei Guo, Zikai Zhang, Yinxia Jiang, Hao Wu, Yongkang Jia, Yantao Dou, Zhicheng Cao, Zhao Distributed and Parallel Software Lab Huawei China Gaoling School of Artificial Intelligence Renmin University of China

Pre-trained model such as BERT has been proved to be an effective tool for dealing with Information Retrieval (IR) problems. Due to its inspiring performance, it has been widely used to tackle with real-world IR problems such as document ranking. Recently, researchers have found that selecting "hard" rather than"random" negative samples would be beneficial for fine-tuning pre-trained models on ranking tasks. However, it remains elusive how to leverage hard negative samples in a principled way. To address aforementioned issues, we propose a fine-tuning strategy for document ranking, namely Self-Involvement Ranker (SIR), to dynamically select hard negative samples to construct high-quality semantic space for training a high-quality ranking model. Specifically, SIR consists of sequential compressors implemented with pre-trained models. Front compressor selects hard negative samples for rear compressor. Moreover, SIR leverages supervisory signal to adaptively adjust semantic space of negative samples. Finally, supervisory signal in rear compressor is computed based on condition probability and thus can control sample dynamic and further enhance the model performance. SIR is a lightweight and general framework for pre-trained models, which simplifies the ranking process in industry practice. We test our proposed solution on MS MARCO with document ranking setting, and the results show that SIR can significantly improve the ranking performance of various pre-trained models. Moreover, our method became the new SOTA model anonymously on MS MARCO Document ranking leaderboard in May 2021. Copyright © 2021, The Authors. All rights reserved.

关键词： Search engines

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Detailed Analysis and Optimization of Irregular-Shaped Matrix Multiplication on Multi-Core DSPs 24

Detailed Analysis and Optimization of Irregular-Shaped Matri...

引用

53rd International Conference on parallel Processing (ICPP)

作者： Mo, Haotian Wang, Qinglin Liao, Linyu Li, Biao Chi, Lihua Liu, Jie Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha Peoples R China Hunan GuoKe Computil Technol Co Ltd Changsha Peoples R China

ISBN: (纸本)9798400717932

Irregular-shaped General Matrix Multiplication (GEMM) is extensively used in diverse workloads, such as scientific simulation and deep learning. In response to energy efficiency constraints, low-power multi-core digital signal processors (DSPs) have emerged as a viable alternative architecture in HPC systems. This study examines the performance of existing GEMM implementations working on irregular-shaped matrices and observes sub-optimal outcomes due to deficiencies in memory optimization and core-level parallelism extraction. For multi-core DSPs in FT-M7032, a CPU-DSP heterogeneous processor for HPC, we introduce dspIMM - a new multi-core parallel implementation for irregular-shaped matrix multiplications. dspIMM incorporates a new loop ordering, stack-space optimization, multi-dimensional core-level parallelization, a communication-computation overlap implementation with data prefetch across loops, and blocking optimization. These optimizations effectively enhance memory access and the core-level parallelism in irregular-shaped GEMMs. Experimental results demonstrate that the proposed communication-computation overlap optimization achieves the highest performance improvement in dspIMM, and the average speedup of dspIMM over previous implementations finally achieves up to 3.34 times.

关键词： Matrix-matrix multiplication Irregular-shaped matrix DSPs parallel algorithm Performance optimization

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Bi-Objective Scheduling Algorithm for Hybrid Workflow in JointCloud 15

Bi-Objective Scheduling Algorithm for Hybrid Workflow in Joi...

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15th IEEE International Conference on Joint Cloud Computing (JCC)

作者： Li, Rui Wang, Huaimin Shi, Peichang Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci State Key Lab Complex & Crit Software Environm Changsha 410073 Peoples R China

ISBN: (纸本)9798350387339

Big data workflows are widely used in IoT, recommended systems, and real-time vision applications, and they continue to grow in complexity. These hybrid workflows consist of both resource-intensive batch jobs and latency-sensitive stream jobs. Examples include the data analytics workflow, which incorporates batch data transformations and low-latency querying, and the machine learning workflow, which processes stream data feature extraction before performing batch training and low-latency inference. However, existing research on workflow scheduling primarily focuses on either stream or batch workflows, neglecting the efficient scheduling of hybrid workflows that respect their diverse resource requirements and the costly data transfers between them. In this article, we propose a hybrid workflow model that defines the optimal placement of hybrid workflows (OHWP) as a bi-objective optimization problem. Our proposed model takes into account parameters related to inter-communication between stream and batch jobs, as well as the heterogeneous resources in JointCloud environment. Additionally, we present OHWP-PS (OHWP on a Pruned Space), a scheduling algorithm for hybrid workflows that minimizes both cost and latency by improving the initial population and dynamically updating the search space. The results demonstrate that the proposed OHWP-PS algorithm is effective and competitive across all experiments.

关键词： batch and stream workflow scheduling hybrid workflow JointCloud bi-object optimization

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