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E-Bus-Based Standby Energy Sharing for EVs in the Event of Large-Scale Grid Outage

IEEE INTERNET OF THINGS JOURNAL 2024年第23期11卷 37885-37898页

作者： Fan, Haitao Hei, Mengzhe Chen, Zuyu Xing, Yijia Hu, Yujiao Guo, Deke Zhang, Xin Zhao, Xiang Pan, Yan Cent South Univ Forestry & Technol Sch Comp Sci & Math Changsha 410004 Peoples R China Natl Univ Def Technol Natl Key Lab Informat Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Lab Big Data & Decis Changsha 410073 Peoples R China Future Network Res Ctr Purple Mt Labs Nanjing 211111 Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China

Electric vehicles (EVs), which have become one of the most important commuting vehicles in the world, heavily depend on the support of robust and efficient grids. Many researchers focus on optimizing the charging of EVs by the grid. However, they overlook the emergency charging when the large-scale grid fails. Inspired by the DC-2-DC technology, EVs can achieve vehicle-2-vehicle (V2V) energy sharing between each other, which inspires us to study leveraging the significant number of electric buses (e-buses) as effective and cost-efficient standby energy to charge low-power EVs when a large-scale grid outage occurs, whereas the feasibility, scheduling algorithm, and performance of e-bus-based V2V energy sharing remain open questions. With this in mind, we first reveal that it is feasible for e-buses to serve as effective standby power for low-power EVs, after careful analysis of real-world data. Then, we conduct a data-driven estimation of the energy consumption to determine the volume of the V2V shared energy for large-scale EVs. Accordingly, we formulate the e-bus-based V2V energy sharing problem, whose two objectives are to maximize the total number of the sufficiently charged EVs and to minimize the distance of these EVs moving to the e-bus for charging. The first objective is to charge more low-power EVs with the limited energy volume of the e-buses and the second objective is to reduce the range anxiety of the low-power EV drivers. Observing that existing algorithm is time-consuming or inefficient, which cannot be applied for the problem instance with a large number of e-buses and EVs, we design a low-complexity approximation algorithm for the problem. Both theoretical analysis and comprehensive data-driven evaluation demonstrate that the proposed algorithm, 1) significantly optimizes the optimization objectives by 23.6% and 15.8% on average, and up to 34.8% and 34.0% in extreme cases, compared with the existing approximation algorithm and 2) achieves over 100x

关键词： Charging stations Approximation algorithms Roads Vehicles Costs Electric vehicle charging Energy consumption Charging distance (CD) data driven E-bus energy sharing E-taxi GAP grid outage heuristic algorithm vehicle-2-vehicle (V2V)

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An efficient communication strategy for massively parallel computation in CFD

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JOURNAL OF SUPERCOMPUTING 2023年第7期79卷 7560-7583页

作者： Wan, YunBo He, Lei Zhang, Yong Zhao, Zhong Liu, Jie Zhang, HaoYuan Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Hunan Peoples R China Computat Aerodynam Inst China Aerodynam Res & Dev Ctr Mianyang 621000 Sichuan Peoples R China

With the development of high-performance computers, it is necessary to develop efficient parallel algorithms in the field of computational fluid dynamics (CFD). In this study, a novel parallel communication strategy based on asynchronous and packaged communication is proposed. The strategy implements an aggregated communication process, which requires only one communication in each iteration step, significantly reducing the number of communications. The correctness and convergence of the novel strategy are demonstrated from both theoretical and experimental perspectives. And based on the real vehicle CHN-T model with 140 million meshes, a detailed performance comparison and analysis is performed for the novel strategy and the traditional strategy, showing that the novel strategy has significant advantages in terms of scalability. Finally, the strong scalability and weak scalability tests are carried out separately for the CHN-T model. The strong scaling efficiency can reach 74% with 10.5 billion meshes and 256,000 cores. The weak scaling parallel efficiency can reach 90% with 10 billion meshes and 179,000 cores. This research work has laid an important foundation for the development of the fast design of aircraft and cutting-edge numerical methods.

关键词： CFD Massively parallel computing Asynchronous communication Scalability Tens of billion grid

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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...

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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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Pioneering Cooperative Air-Ground Instant Delivery Using UAVs and Crowdsourced Couriers

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procEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2024年第4期8卷 1-26页

作者： Pan, Yen Gao, Junhui Duan, Jiashun Shi, Juan Guo, Bin Liang, Yunji Hu, Yujiao Natl Univ Def Technol Natl Key Lab Informat Syst Engn Changsha Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Air Force Engn Univ Xian Peoples R China Purple Mt Labs Future Network Res Ctr Nanjing Peoples R China

Instant delivery, which has a strict time limit, has become a fundamental service in modern cities. Increasing demands and labor shortages make efficient instant delivery more challenging. Recently, the industry has recruited Unmanned Aerial Vehicles (UAVs) and part-time couriers to address this critical issue. However, the inherent delivery ability of crowdsourced couriers and the limited battery capacity of UAVs make them hard to solely meet the rapidly increasing instant delivery demands. Therefore, this paper proposes the first air-ground cooperative UAV and Courier Delivery paradigm, called UCD, to develop an optimal courier recruitment plan and assignment of instant delivery tasks for UAVs and couriers. In the UCD paradigm, courier and UAV delivery models are introduced, and a recruitment plan that considers both delivery demand and courier availability is designed. With this foundation, a data-driven algorithm is proposed to optimize delivery task assignments, maximizing the flexibility of UAVs and the capacity of couriers. Comprehensive evaluations are conducted on multiple long-term real-world datasets to demonstrate the superior performance of UCD.

关键词： UAV Courier Data-driven Air-Ground Instant Delivery Cooperative Delivery

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MT-office: parallel password recovery program for office on domestic heterogeneous multi-core processor

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CCF TRANSACTIONS ON HIGH PERFORMANCE COMPUTING 2023年第3期5卷 231-244页

作者： Luo, Yongtao Yang, Bo Liu, Jie Wang, Ruibo Wen, Jinmin Xiao, Tiaojie Chen, Xuguang Gong, Chunye Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Digitizing Program Frontier Equipment Changsha 410073 Peoples R China Jinan Univ Sch Informat Sci & Technol Guangzhou 510632 Peoples R China

With the improvement of security awareness, in order to guarantee information security, more advanced and secure encryption algorithms are applied to Microsoft Office. People also set more complex encryption passwords. However, once the initial password is forgotten, the encrypted information needs to be retrieved. The conventional brute force cracking methods and password recovery programs can hardly meet the actual deciphering needs. To this end, we develop a distributed parallel password recovery program (MT-Office) for Microsoft Office on the domestic heterogeneous multi-core processor (MT-3000). MT-Office takes full advantage of the multi-core and heterogeneous features of MT-3000, and is optimized and improved in both vectorization and global computing. At the same time, MT-Office provides multiple recovery strategies in password generation to improve the recovery efficiency. Compared with other platforms (e.g., Intel platforms and FT platforms), MT-3000 heterogeneous platform can achieve 60 x -218 x speedup ratio. For Office2010, we perform a strong scalability test on the new-generation supercomputer in National Supercomputer Center in Tianjin. MT-Office not only extends to 65,536 acceleration clusters on this system, shows good scalability, but also achieves almost linear speedup ratio. For Office2007, compared with other password recovery programs, MT-Office can achieve 2.5 x -131.1 x speedup ratio. It can be seen that MT-Office can better exploit the advantages of MT-3000, which not only has good scalability and parallelism, but also has faster deciphering speed and can be applied to practical engineering application.

关键词： Office password recovery Heterogeneous multi-core processor SIMD Heterogeneous computing

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Exploring Policy Diversity in parallel Actor-Critic Learning 34

Exploring Policy Diversity in Parallel Actor-Critic Learning

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34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Zhang, Yanqiang Zhai, Yuanzhao Zhou, Gongqian Ding, Bo Feng, Dawei Liu, Songwang Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Peoples R China Acad Mil Sci Beijing Peoples R China

ISBN: (纸本)9798350397444

Exploration is a critical challenge for deep reinforcement learning methods. Although existing works such as actor-critic algorithms have made much progress, most still suffer from the sample inefficiency problem in complex environments where rewards are sparse. parallel sampling, which uses multiple actors with the same policy interacting with the environment, is an effective approach to improve sample efficiency. However, parallel parameter-sharing actors collect similar samples, which generally hinders the improvement of the overall exploration process. In this paper, we propose a Policy Diversity enhanced approach for parallel Actor-Critic (PDAC). Specifically, we extend the parallel actor-critic architecture to the PDAC framework composed of a shared critic and parallel distinct actors. Then we introduce the KL-divergence of the action probability distribution between parallel actors as the intrinsic reward to encourage actors to explore diverse strategies. We evaluate our approach in multiple challenging procedurally-generated tasks and compare it with state-of-the-art algorithms. Experiments show that PDAC makes significant progress in the comparison, in terms of cumulative rewards and sample efficiency.

关键词： actor-critic exploration efficiency diversity promotion

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Ancient poetry generation with an unsupervised method

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NEURAL COMPUTING & APPLICATIONS 2022年第11期34卷 8525-8538页

作者： Zhang, Zhanjun Zhang, Haoyu Wan, Qian Jia, Xiangyu Zhang, Zhe Liu, Jie Natl Univ Def Technol Coll Comp Changsha Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha Peoples R China

It is challenging to use unsupervised machine translation models to generate ancient poems. The current method has solved the problems of Under-translation and Over-translation caused by the huge length difference between the translated sentence pairs. However, the above method lacks guidance in generating intermediate vectors, and the denoising ability of the model is very poor. In this paper, we guide vector space distribution during training to improve the quality of the generated ancient poems and the convergence speed of the model. We also introduce the target language information while adding noise, which effectively avoids the recurrence of the Under-translation problem while improving the model's denoising ability. Experiment results on the VP dataset show that our model obtains state-of-the-art results with faster convergence speed. In addition to the BLEU scores, we also made a comparative analysis of ancient poetry sentences generated by different models. The analysis results show that the optimization method proposed in this paper is indeed helpful for generating high-quality ancient poems.

关键词： Unsupervised Generate ancient poems Adding noise Guidance

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Temporal Extrapolation and Knowledge Transfer for Lifelong Temporal Knowledge Graph Reasoning

Temporal Extrapolation and Knowledge Transfer for Lifelong T...

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Conference on Empirical Methods in Natural Language processing (EMNLP)

作者： Chen, Zhongwu Xu, Chengjin Su, Fenglong Huang, Zhen Dou, Yong Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Comp Changsha Peoples R China Int Digital Econ Acad IDEA Shenzhen Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9798891760615

Real-world Temporal Knowledge Graphs keep growing with time and new entities and facts emerge continually, necessitating a model that can extrapolate to future timestamps and transfer knowledge for new components. Therefore, our work first dives into this more realistic issue, lifelong TKG reasoning, where existing methods can only address part of the challenges. Specifically, we formulate lifelong TKG reasoning as a temporal-path-based reinforcement learning (RL) framework. Then, we add temporal displacement into the action space of RL to extrapolate for the future and further propose a temporal-rule-based reward shaping to guide the training. To transfer and update knowledge, we design a new edge-aware message passing module, where the embeddings of new entities and edges are inductive. We conduct extensive experiments on three newly constructed benchmarks for lifelong TKG reasoning. Experimental results show the outperforming effectiveness of our model against all well-adapted baselines.

关键词： Knowledge graph

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Optimizing Pointwise Convolutions on Multi-core DSPs 1

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23rd International Conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Wang, Yang Wang, Qinglin Pei, Xiangdong Mei, Songzhu Liu, Jie Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Beijing Inst Astronaut Syst Engn Beijing 100076 Peoples R China

ISBN: (数字)9789819708628

ISBN: (纸本)9789819708611;9789819708628

Pointwise convolutions are widely used in various convolutional neural networks, due to low computation complexity and parameter requirements. However, pointwise convolutions are still time-consuming like regular convolutions. As a result of increasing power consumption, low-power embedded processors have been brought into high-performance computing field, such as multi-core digital signal processors (DSPs). In this paper, we propose a high-performance multi-level parallel direct implementation of pointwise convolutions on multi-core DSPs in FT-M7032, a CPU-DSP heterogeneous prototype processor. The main optimizations include on-chip memory blocking, loop ordering, vectorization, register blocking, and multi-core parallelization. The experimental results show that the proposed direct implementation achieves much better performance than GEMM-based ones on FT-M7032, and a speedup of up to 79.26 times is achieved.

关键词： CNNs Pointwise Convolution Direct Convolution DSPs parallel algorithm

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Hippie: A Data-paralleled Pipeline Approach to Improve Memory-Efficiency and Scalability for Large DNN Training 21

Hippie: A Data-Paralleled Pipeline Approach to Improve Memor...

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50th International Conference on parallel processing (ICPP)

作者： Ye, Xiangyu Lai, Zhiquan Li, Shengwei Cai, Lei Sun, Ding Qiao, Linbo Li, Dongsheng Natl Univ Def Technol Comp Coll Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781450390682

With the increase of both data and parameter volume, it has become a big challenge to efficiently train large-scale DNN models on distributed platforms. Ordinary parallelism modes, i.e., data parallelism, model parallelism and pipeline parallelism, can no longer satisfy the efficient scaling of large DNN model training on multiple nodes. Meanwhile, the problem of too much memory consumption seriously restricts GPU computing efficiency and training throughput. In this paper, we propose Hippie, a hybrid parallel training framework that integrates pipeline parallelism and data parallelism to improve the memory efficiency and scalability of large DNN training. Hippie adopts a hybrid parallel method based on hiding gradient communication, which improves the throughput and scalability of training. Meanwhile, Hippie introduces the last-stage pipeline scheduling and recomputation for specific layers to effectively reduce the memory overhead and ease the difficulties of training large DNN models on memory-constrained devices. To achieve a more reasonable evaluation of the optimization effect, we propose an index of memory efficiency (ME) to represent the tradeoff between throughput and memory overhead. We implement Hippie based on PyTorch and NCCL. Experiments on various models show that Hippie achieves above 90% scaling efficiency on a 16-GPU platform. Moreover, Hippie increases throughput by up to 80% while saving 57% of memory overhead, achieving 4.18x memory efficiency.

关键词： deep learning hybrid parallelism distributed training memory efficiency

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