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Exploiting contention and congestion aware switch allocation in network-on-chips 17

Exploiting contention and congestion aware switch allocation...

引用

50th ACM Turing Conference - China, ACM TUR-C 2017

作者： Li, Cunlu Dongy, Dezun Liao, Xiangke National Laboratory for Parallel and Distributed Processing Collaborative Innovation Center of High Performance Computing College of Computer National University of Defense Technology Changsha Hunan410073 China

ISBN: (纸本)9781450348737

Network-on-chip system plays an important role to improve the performance of chip multiprocessor systems. As the complexity of the network increases, congestion problem has become the major performance bottleneck and seriously influence the performance of NoCs. Prior works have focused on designing effective routing algorithm based on collecting congestion and contention information to load balance the traffic. However, most prior works do not consider balancing the traffic load during switch allocation. Due to the lack of congestion information in switch allocation stage, switch allocator performs allocation only based on packet requests and thus aggravates the congestion in the ports of switch. In this paper, we propose to add the congestion and contention information into the switch allocation process and design an efficient on-chip switching strategy which utilizes the contention and congestion information to load balance the traffic and achieve efficient switch allocation. To further enhance the performance of our design, we carefully design our switch allocation strategy to balance the trade-off between traffic load balance and the matching efficiency in switch allocation. We evaluate our design under synthetic traffic and trace of parsec benchmarks. Our evaluations show this mechanism achieves optimal latency compared to best previous switch allocation strategies. © 2017 ACM.

关键词： Network-on-chip

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A Data-Centric Approach for Efficient and Scalable CFD Implementation on Multi-GPUs Clusters 24th

A Data-Centric Approach for Efficient and Scalable CFD Imple...

引用

24th International Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2023

作者： Li, Ruitian Deng, Liang Dai, Zhe Zhang, Jian Liu, Jie Liu, Gang China Aerodynamic Research and Development Center Computational Aerodynamic Institute Mianyang China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789819982103

Scalability is a crucial factor determining the performance of massive heterogeneous parallel CFD applications on the multi-GPUs platforms, particularly after the single-GPU implementations have achieved optimal performance through numerous optimizations. A novel Data-Centric hybrid MPI-CUDA CFD model is proposed in this paper to enable efficient scalability of CFD applications on large-scale heterogeneous platforms. Based on the Data-Centric approach, Minimum-cost MPI transfer strategy and the code refactoring technique are realized for a better balance between data transfer and floating-point computation performance, which could significantly improve the scalability and reduce the time-to-solution. Subsequently, those approaches are integrated into the industrial unstructured CFD software, FlowStar, to evaluate their effectiveness. Numerical results demonstrate that Minimum-cost MPI strategy achieves more than 2.0 times performance improvement compared to the traditional Model-Centric implementation, and the code refactoring technique boosts performance by 40% to 50% over the minimum-cost MPI version. Moreover, the Data-Centric implementation on 64 A100 GPUs platform show a speedup ratio of over 120 when compared to the original MPI implementation with 64 ranks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scalability

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Information recycling for social creativity

Information recycling for social creativity

引用

2011 3rd International Conference on Awareness Science and Technology, iCAST 2011

作者： Wu, Yilang Watanabe, Takanobu Distributed Parallel Processing Laboratory University of Aizu Aizu-wakamatsu 965-8580 Japan University Business Innovation Center University of Aizu Aizu-wakamatsu 965-8580 Japan

ISBN: (纸本)9781457708879

Social brainstorming has become a popular group creativity technique to create a large number of ideas for solving problems. The social computing, for its advance in information processing and network communication, collaborates the social intelligence and enhances the social creativity in brainstorming. However, facing the dilemma of quantity deficiency in idea pondering or quality submergence in idea deluge, cooperative participants essentially require needed information for their innovative work rather than unnecessary disturbance. Therefore, in purpose of enhancing the social creativity, we propose the needed information recycling based on multi-features in meta-data and also the similarity in social graph, helping participants keep a global and local image of previous work and focus on innovation tasks. Given an experimental demo, we discuss the essential merits of the novel brainstorming through the comprehensive but collaborative inter-merge of the social network and computer network, and conclude that the needed information recycling breeds the efficient and effective social creativity. © 2011 IEEE.

关键词： Image enhancement

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A Dynamic Mapping Model for General CNN Accelerator Based on FPGA 17th

A Dynamic Mapping Model for General CNN Accelerator Based on...

引用

17th IFIP WG 10.3 International Conference on Network and parallel Computing, NPC 2020

作者： Zhao, Xiaoqiang Jiang, Jingfei Han, Zhe Xu, Jinwei Liu, Zhiqiang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China

ISBN: (纸本)9783030794774

As the application scenarios of convolutional neural network (CNN) become more and more complex, the general CNN accelerator based on matrix multiplication has become a new research focus. The existing mapping methods for converting convolution calculation into matrix multiplication need to be improved. This paper proposes a new dynamic mapping model to improve the flexibility and versatility of matrix multiplication. The dynamic mapping model implements two algorithms: dynamic residue processing mapping algorithm (DRPMA) and dilated convolution mapping algorithm (DCMA). The former can dynamically adjust the mapping method according to the number of output channels of the convolution layer, improve the utilization of the multiply-accumulate (MAC) array. The latter extends the efficient support for Dilated CNNs. For demonstration, we implement an accelerator with Verilog on Xilinx VC709 FPGA board and test some typical CNN models. Experimental results show that the general accelerator achieves high performance and energy efficiency. © 2021, IFIP International Federation for Information processing.

关键词： Field programmable gate arrays (FPGA)

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ParTransgrid: A scalable parallel preprocessing tool for unstructured-grid cell-centered computational fluid dynamics applications

ParTransgrid: A scalable parallel preprocessing tool for uns...

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作者： Zhang, Jian Liu, Jie Zhou, Naichun Tang, Jing He, Xie Chen, Jianqiang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang China

The development of a basic scalable preprocessing tool is the key routine to accelerate the entire computational fluid dynamics (CFD) workflow toward the exascale computing era. In this work, a parallel preprocessing tool, called ParTransgrid, is developed to translate the general grid format like CFD General Notation System into an efficient distributed mesh data format for large-scale parallel computing. Through ParTransgrid, a flexible face-based parallel unstructured mesh data structure designed in Hierarchical Data Format can be obtained to support various cell-centered unstructured CFD solvers. The whole parallel preprocessing operations include parallel grid I/O, parallel mesh partition, and parallel mesh migration, which are linked together to resolve the run-time and memory consumption bottlenecks for increasingly large grid size problems. An inverted index search strategy combined with a multi-master-slave communication paradigm is proposed to improve the pairwise face matching efficiency and reduce the communication overhead when constructing the distributed sparse graph in the phase of parallel mesh partition. And we present a simplified owner update rule to fast the procedure of raw partition boundaries migration and the building of shared faces/nodes communication mapping list between new sub-meshes with an order of magnitude of speed-up. Experiment results reveal that ParTransgrid can be easily scaled to billion-level grid CFD applications, the preparation time for parallel computing with hundreds of thousands of cores is reduced to a few minutes. © 2021 John Wiley & Sons, Ltd.

关键词： Computational fluid dynamics

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RiverMapper: Step-wisely Mapping the Surface Rivers on Optical Remote Sensing Images

RiverMapper: Step-wisely Mapping the Surface Rivers on Optic...

引用

2022 International Conference on Neural Networks, Information, and Communication Engineering, NNICE 2022

作者： Zhang, Peng Pan, Hengyue Yang, Ke Dou, Yong Niu, Xin National Laboratory for Parallel and Distributed Processing National University of Defense Technology 410073 China Artificial Intelligence Research Center National Innovation Institute of Defense Technology BeiJing100850 China

ISBN: (纸本)9781510655171

Accurately mapping the surface rivers is important in ecological environment monitoring and disaster prevention. The development of remote sensing technology and computer vision greatly improves the efficiency of this task. However, there are few methods that map the rivers from an image directly. The existing automatic river mapping methods usually had two successive stages: waterbody extraction and flow-path extraction, where the latter methods were very dependent on the waterbody masks generated by the former methods. Errors in waterbody masks caused breaks and redundancies in the extracted graphs. This paper proposed RiverMapper, which mapped the rivers step-wisely without dividing into two stages. Following the directions and actions predicted by the convolution neural network, RiverMapper walked along the rivers step by step and cropped the fixed-size image patches at each step for segmentation. Final river graphs were constructed by the waterbody mask patches and those tracks generated by RiverMapper. We applied RiverMapper on optical remote sensing images containing the Changjiang River and the Huanghe River. Without the degradation of the performance on waterbody extraction, RiverMapper outperformed other methods in terms of the local topological and geometrical similarity between the predicted and the ground-truth river graphs. © 2022 SPIE.

关键词： Mapping

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parallel Implementation of SHA256 on Multizone Heterogeneous Systems 21

Parallel Implementation of SHA256 on Multizone Heterogeneous...

引用

21st IEEE International Symposium on parallel and distributed processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Luo, Yongtao Liu, Jie Xiao, Tiaojie Gong, Chunye National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Program for Frontier Equipment Changsha China National Supercomputer Center in Tianjin Tianjin China

ISBN: (纸本)9798350329223

SHA-256 plays an important role in widely used applications, such as data security, data integrity, digital signatures, and cryptocurrencies. However, most of the current optimized implementations of SHA-256 are based on CPUs or dedicated hardware, such as ASICs and FPGAs. Consequently, there is a need to explore whether new heterogeneous parallel framework can improve the computational performance of the hash function. To address this issue, we conducted a study on the MT-3000 platform, which is a special architecture processor for the next-generation exascale prototype supercomputer. We proposed MT-SHA256, a heterogeneous multistage parallel implementation for hashing multiple messages on the MT-3000. Combining the architectural features of this processor, we developed an effective solution that significantly improved the computational performance of SHA-256. As a result, MT-SHA256 achieved a maximum throughput of 1045.68 MB/s on a single acceleration core of MT-3000. This is 9.84x higher than the C code implementation on one CPU core of MT-3000. We also performed a scalability test and found that MT-SHA256 achieved a throughput of 98.04 GB/s on a computing node, and extended to 512 nodes (2048 acceleration clusters) on this system with good scalability. © 2023 IEEE.

关键词： Hash functions

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A wallet-size cluster for H.264 encoding

A wallet-size cluster for H.264 encoding

引用

3rd International Conference on Information Technology and Applications, ICITA 2005

作者： Lee, Liang-Teh Tseng, Chia-Ying Liu, Kang-Yuan Li, Kuan-Ching Dept. of Computer Science and Engineering Tatung University Taipei 104 Taiwan Parallel and Distributed Processing Center Dept. of Computer Science and Information Management Providence University Taichung 433 Taiwan

ISBN: (纸本)0769523161

A scalable wallet-size cluster computing system has been proposed to obtain low cost, low power consumption and small sized digital home processing center. In this paper, we apply the wallet-size cluster for H.264 encoding to accelerate the H.264 video processing. We analyze the encoder scheme of the H.264, and perform the H.264 encoding in parallel through the nodes of the cluster. The experimental results show that an efficient real time video processing for H.264 can be achieved. © 2005 IEEE.

关键词： Video signal processing

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Pruning false positives of static data-race detection via thread specialization

Pruning false positives of static data-race detection via th...

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10th International Symposium on Advanced parallel processing Technologies, APPT 2013

作者： Chen, Chen Lu, Kai Wang, Xiaoping Zhou, Xu Fang, Li National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China Information Center National University of Defense Technology Changsha China

ISBN: (纸本)9783642452925

Static data-race detection is a powerful tool by providing clues for dynamic approaches to only instrument certain memory accesses. However, static data-race analysis suffers from high false positive rate. A key reason is that static analysis overestimates the set of shared objects a thread can access. We propose thread specialization to distinguish threads statically. By fixing the number of threads as well as the ID assigned to each thread, a program can be transformed to a simplified version. Static data-race analysis on this simplified program can infer the range of addresses accessed by each thread more accurately. Our approach prunes false positives by an average of 89.2% and reduces dynamic instrumentation by an average of 63.4% in seven benchmarks. © 2013 Springer-Verlag.

关键词： Static analysis

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Location-Aware Multi-user Resource Allocation in distributed Clouds

Location-Aware Multi-user Resource Allocation in Distributed...

引用

10th Annual Conference of Advanced Computer Architecture, ACA 2014

作者： Li, Jiaxin Li, Dongsheng Zheng, Jing Quan, Yong National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Information Center of Logistics Department Beijing China School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9783662444900

Resource allocation for multi-user across multiple data centers is an important problem in cloud computing environments. Many geographically-distributed users may request virtualized resources simultaneously. And the distances from users to allocated resources have much impact on the quality of service (QoS) in multiple data centers environment. Most existing methods do not take all these factors into account when allocating resources. They usually result in poor runtime performance of users' virtual computing environment and the remarkable difference of users' QoS. In this paper, we propose RAMD, a resource allocation algorithm based on multi-stage decision in multiple data centers. The RAMD algorithm allocate VMs to users, taking into account the correlation and interaction between multiple users, so as to minimize the sum of all users' service distances (i.e. determined by user location and network distance of virtual machines). Experimental results show that the algorithm can effectively deal with the cloud resource allocation for multi-user across multiple data centers. It can improve the runtime performance of users' virtualized resources and reduce the difference of QoS. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Resource allocation

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