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5th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2022

作者： Wang, Hao Feng, Dawei Ding, Bo Li, Wei College of Computer National University of Defense Technology Changsha Hunan China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Independent Researcher GuangZhou China

ISBN: (纸本)9781450398343

Offline imitative learning(OIL) is often used to solve complex continuous decision-making tasks. For these tasks such as robot control, automatic driving and etc., it is either difficult to design an effective reward for learning or very expensive and time-consuming for agents to collect data interactively with the environment. However, the data used in previous OIL methods are all gathered by reinforcement learning algorithms guided by task-specific rewards, which is not a true reward-free premise and still suffers from the problem of designing an effective reward function in real tasks. To this end, we propose the reward-free exploratory data driven offline imitation learning (ExDOIL) framework. ExDOIL first trains an unsupervised reinforcement learning agent by interacting with the environment, and collects enough unsupervised exploration data during training;Then, a task independent yet simple and efficient reward function is used to relabel the collected data;Finally, an agent is trained to imitate the expert to complete the task through a conventional RL algorithm such as TD3. Extensive experiments on continuous control tasks demonstrate that the proposed framework can achieve better imitation performance(28% higher episode returns on average) comparing with previous SOTA method(ORIL) without any task-specific rewards. © 2022 ACM.

关键词： Reinforcement learning

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Representation learning on textual network with personalized Page Rank

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science China(Information sciences) 2021年第11期64卷 95-104页

作者： Teng LI Yong DOU National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Representation learning on textual network or textual network embedding, which leverages rich textual information associated with the network structure to learn low-dimensional embedding of vertices, has been useful in a variety of tasks. However, most approaches learn textual network embedding by using direct neighbors. In this paper, we employ a powerful and spatially localized operation: personalized Page Rank(PPR) to eliminate the restriction of using only the direct connection relationship. Also, we analyze the relationship between PPR and spectral-domain theory, which provides insight into the empirical performance boost. From the experiment, we discovered that the proposed method provides a great improvement in linkprediction tasks, when compared to existing methods, achieving a new state-of-the-art on several real-world benchmark datasets.

关键词： representation learning network embedding PageRank textual network personalized PageRank

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Prophet: Fine-grained Load Balancing for parallel Training of Large-scale MoE Models

Prophet: Fine-grained Load Balancing for Parallel Training o...

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IEEE International Conference on Cluster Computing

作者： Wei Wang Zhiquan Lai Shengwei Li Weijie Liu Keshi Ge Yujie Liu Ao Shen Dongsheng Li National Laboratory for Parallel and Distributed Processing(PDL) College Of Computer National University Of Defense Technology Changsha China

Mixture of Expert (MoE) has received increasing attention for scaling DNN models to extra-large size with negligible increases in computation. The MoE model has achieved the highest accuracy in several domains. However, a significant load imbalance occurs in the device during the training of a MoE model, resulting in significantly reduced throughput. Previous works on load balancing either harm model convergence or suffer from high execution overhead. To address these issues, we present Prophet: a fine-grained load balancing method for parallel training of large-scale MoE models, which consists of a planner and a scheduler. Prophet planner first employs a fine-grained resource allocation method to determine the possible scenarios for the expert placement in a fine-grained manner, and then efficiently searches for a well-balanced expert placement to balance the load without introducing additional overhead. Prophet scheduler exploits the locality of the token distribution to schedule the resource allocation operations using a layer-wise fine-grained schedule strategy to hide their overhead. We conduct extensive experiments in four clusters and five representative models. The results indicate that Prophet gains up to 2.3x speedup compared to the state-of-the-art MoE frameworks including Deepspeed-MoE and FasterMoE. Additionally, Prophet achieves a load balancing enhancement of up to 12.06x when compared to FasterMoE.

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SMVAR: A Novel RNN Accelerator Based on Non-blocking Data Distribution Structure

SMVAR: A Novel RNN Accelerator Based on Non-blocking Data Di...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Jinwei Xu Jingfei Jiang Shiyao Xu Lei Gao Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha Hunan China

Recurrent neural networks (RNNs) have become common models in the field of artificial intelligence to process temporal sequence task, such as speech recognition, text analysis, natural language processing, etc. To speedup RNNs inference, previous research proposed model sparse pruning techniques. However, the pruning rate of existing sparse pruning algorithms will be affected by pruning granularity and hardware friendliness. In order to approximate nonstructured pruning algorithm, this paper proposes Large Region Balanced Sparse (LRBS) pruning method, which does not limit sub-matrix shape and effectively improves pruning rate. Furthermore, we propose Sparse Matrix Vector Multiplication Accelerator for RNNs (SMVAR), which adopt non-blocking data distribution structure to solve the problem of efficient execution of large region irreg-ular matrix multiplication. To further improve the accelerator performance, SMVAR fine-grained adjusts the pipeline between macro-operations to reduce the idle of compute components. In addition, according to the coarse-grained block characteristics of LRBS algorithm, we develop the coarse-grained parallelism of accelerator with multiply compute units(CUs) structure. Experiments show that the pruning rate of our proposed LRBS is 1.25x-2.5x higher than that of the existing pruning algorithms. Compared with the existing work, the execution efficiency is improved by more than 2.02x-35.9x in the same application scenario.

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Prediction of the Cyanobacteria Coverage in Time-series Images based on Convolutional Neural Network 21

Prediction of the Cyanobacteria Coverage in Time-series Imag...

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4th International Conference on Control and Computer Vision, ICCCV 2021

作者： Ye, Xiangyu Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology China

ISBN: (纸本)9781450390477

In recent years, the problem of lake eutrophication has become increasingly severe. The monitoring and control of cyanobacteria in lakes are of great significance. The information obtained by existing monitoring methods is relatively lagging, and it is impossible to monitor the sudden outbreak of cyanobacteria in time. Getting cyanobacteria information directly through camera images is a breakthrough. In this paper, after analyzing the characteristics of time series cyanobacteria images, we propose a block prediction scheme based on the CNN model. Experiments show that this method can quickly calculate the coverage of cyanobacteria in the monitoring image in a short time. It can also effectively distinguish cyanobacteria-rich water areas, which significantly facilitates water quality monitoring and cyanobacteria management. We can draw a chart of the changes in the coverage of cyanobacteria by analyzing multi-day time-series images. The chart helps us conduct a short-term water quality analysis to better deal with the outbreak of cyanobacteria. © 2021 ACM.

关键词： Lakes

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Optimizing Batched Small Matrix Multiplication on Multi-core DSP Architecture 22

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

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22nd IEEE International Symposium on parallel and distributed processing with Applications, ISPA 2024

作者： Zuo, Xiaohan Xiao, Chunhua Wang, Qinglin Shi, Chen Chongqing University College of Computer Science Chongqing China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha410073 China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment Changsha410073 China

ISBN: (纸本)9798331509712

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. © 2024 IEEE.

关键词： Network security

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ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations

ST-PINN: A Self-Training Physics-Informed Neural Network for...

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International Joint Conference on Neural Networks (IJCNN)

作者： Junjun Yan Xinhai Chen Zhichao Wang Enqiang Zhoui Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and convergence problems of existing PINNs, we propose a self-training physics-informed neural network, ST-PINN. Specifically, ST-PINN introduces a pseudo label based self-learning algorithm during training. It employs governing equation as the pseudo-labeled evaluation index and selects the highest confidence examples from the sample points to attach the pseudo labels. To our best knowledge, we are the first to incorporate a self-training mechanism into physics-informed learning. We conduct experiments on five PDE problems in different fields and scenarios. The results demonstrate that the proposed method allows the network to learn more physical information and benefit convergence. The ST-PINN outperforms existing physics-informed neural network methods and improves the accuracy by a factor of 1.33x-2.54x.

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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MPS: A Multiple Poisoned Samples Selection Strategy in Backdoor Attack

MPS: A Multiple Poisoned Samples Selection Strategy in Backd...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weihong Zou Shigeng Zhang Weiping Wang Jian Zhang Xuan Liu Schoosl of Computer Science and Engineering Central South University Science and Technology on Parallel and Distributed Processing Laboratory (PDL) College of Computer Science and Electronic Engineering Hunan University

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Recently there has been many studies on backdoor attacks, which involve injecting poisoned samples into the training set in order to embed backdoors into the model. Existing multiple poisoned samples attacks usually randomly select a subset from clean samples to generate the poisoned samples. Filtering-and-Updating Strategy (FUS) has shown that the poisoning efficiency of each poisoned sample is inconsistent and random selection is not optimal. However, FUS does not fully considered the selection of multiple poisoned samples, there are still some issues with the selection of multiple poisoned samples. In this paper, we formulate the selection of multiple types of poisoned samples as a multi-objective optimization problem and proposed a Multiple Poisoned Samples Selection Strategy (MPS) to solve the issue. Unlike FUS, we consider the potential of clean samples that are not selected as to become efficient poisoned samples. Specifically, we use a weight-based contribution approach to calculate the contribution of each sample (clean sample and poisoned sample) during the training process from multiple dimensions. Finally, based on the greedy approach, we retain a subset of samples with the largest contribution in each dimension through iterations. We evaluate the effectiveness of MPS on various attack methods, including BadNet, Blended, ISSBA, and WaNet, as well as benchmark datasets. The experimental results on CIFAR-10 and GTSRB show that MPS can increase the attack strength by 1.45% to 18.34% compared to RSS and 0.43% to 10.84% compared to FUS in multiple poisoned samples attacks, thereby enhancing the stealthiness of the attack. Meanwhile, MPS is suitable for black-box settings, meaning that poisoned samples selected in one setting can be applied to other settings.

关键词： Training Data privacy Closed box Benchmark testing Security Protection Optimization

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parallel Implementation of SHA256 on Multizone Heterogeneous Systems

Parallel Implementation of SHA256 on Multizone Heterogeneous...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

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

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.

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