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FlowEmbed: Binary function embedding model based on relational control flow graph and byte sequence 29

FlowEmbed: Binary function embedding model based on relation...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Wang, Yongpan Dong, Chaopeng Li, Siyuan Luo, Fucai Su, Renjie Song, Zhanwei Li, Hong Chinese Academy of Sciences Institute of Information Engineering China University of Chinese Academy of Sciences School of Cyber Security China State Grid Fujian Electric Power Company China

ISBN: (纸本)9798350330717

Binary function embedding models are applicable to various downstream tasks within IoT device software systems and have demonstrated advantages in numerous binary analysis tasks, such as vulnerability (homologous) function search and compilation optimization option identification. However, current binary function embedding methods either learn embedding based on code sequence, which lack the program semantics of functions (e.g., control flow, etc.) or based on program structure graphs, which omit global sequential information. As a result, these methods fall short in enabling models to learn the complete semantic of function. In this paper, we introduce FlowEmbed, a novel approach that synergistically integrates control flow and global semantic learning to facilitate exhaustive code comprehension. Initially, FlowEmbed harnesses a distinct relational control flow graph combined with the power of BERT and RGCN models to aptly capture the nuances of control flow semantics. Moreover, by deploying the DPCNN model on a byte sequence constructed from function machine code, FlowEmbed adeptly discerns the inherent global sequential semantics of binary functions. Through rigorous evaluations spanning three IoT-related tasks, FlowEmbed's efficacy becomes evident, showcasing notable improvements: a 20.6% improvement in compilation optimization option identification, a 1.8% improvement in binary function similarity analysis, and an 11.9% improvement in homologous function search. Collectively, these results underscore FlowEmbed's superior capability, positioning it as a invaluable asset in a binary analysis application. © 2023 IEEE.

关键词： binary code search binary code similarity detection binary function embedding deep learning static analysis

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AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algorithm on the New Sunway Supercomputer 29

AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algori...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Tian, Min Liu, Qi Li, Yanlong Du, Wei Ren, Zenghui Liu, Yue Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

The solution of tridiagonal linear systems is used in in various fields and plays a crucial role in numerical simulations. However, there is few efficient solver for tridiagonal linear systems on the new Sunway supercomputer. based on a three-dimensional heat conduction problem, we propose an adaptive heterogeneous tridiagonal matrix algorithm (AH-TDMA). Our major innovations include: (1) To address computational hotspots within AH-TDMA, a multi-level parallel approach involving MPI+Athread has been adopted. (2) An adaptive data partitioning scheme has been set up to achieve load balance. (3) Employing direct memory access and establishing shared space between registers and main memory, instead of employing discrete memory access, is done to enhance memory access efficiency. (4) The optimization of the loop structure has been made in adjusting the sequencing of the dual-layered loops to reduce communication overhead. The experimental results show that, with one core group, the AH-TDMA achieves a speedup of 99.6 times for hotspot and total time speedup up to 59.4 times compared to the parallel and Scalable Library for Tridiagonal Matrix Algorithm (PaScal TDMA). The AH-TDMA is scalable up to 2048 core groups, with a parallel efficiency of 69.2%. © 2023 IEEE.

关键词： adaptive heterogeneous algorithm MPI+Athread Sunway supercomputer three-dimensional heat conduction problem tridiagonal linear systems

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AccDP: Accelerated Data-parallel distributed DNN Training for Modern GPU-based HPC Clusters 29

AccDP: Accelerated Data-Parallel Distributed DNN Training fo...

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29th Annual IEEE International conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Alnaasan, Nawras Jain, Arpan Shafi, Aamir Subramoni, Hari Panda, Dhabaleswar K. Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781665494236

Deep Learning (DL) has become a prominent machine learning technique due to the availability of efficient computational resources in the form of Graphics processing Units (GPUs), large-scale datasets and a variety of models. The newer generation of GPUs are being designed with special emphasis on optimizing performance for DL applications. Also, the availability of easy-to-use DL frameworks-like PyTorch and TensorFlowhas enhanced productivity of domain experts to work on their custom DL applications from diverse domains. However, existing Deep Neural network (DNN) training approaches may not fully utilize the newly emerging powerful GPUs like the NVIDIA A100-this is the primary issue that we address in this paper. Our motivating analyses show that the GPU utilization on NVIDIA A100 can be as low as 43% using traditional DNN training approaches for small-to-medium DL models and input data size. This paper proposes AccDP-a data-parallel distributed DNN training approach-to accelerate GPU-based DL applications. AccDP exploits the Message Passing Interface (MPI) communication library coupled with the NVIDIA's Multi-Process Service (MPS) to increase the amount of work assigned to parallel GPUs resulting in higher utilization of compute resources. We evaluate our proposed design on different small-to-medium DL models and input sizes on the state-of-the-art HPC clusters. By injecting more parallelism into DNN training using our approach, the evaluation shows up to 58% improvement in training performance on a single GPU and up to 62% on 16 GPUs compared to regular DNN training. Furthermore, we conduct an in-depth characterization to determine the impact of several DNN training factors and best practices-including the batch size and the number of data loading workers- to optimally utilize GPU devices. To the best of our knowledge, this is the first work that explores the use of MPS and MPI to maximize the utilization of GPUs in distributed DNN training.

关键词： Deep Neural networks Graphics processing Units Multi-Process Service MVAPICH2

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Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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MACSQ: Massively Accelerated DeepQ Learning on GPUs Using On-the-fly State Construction 22nd

MACSQ: Massively Accelerated DeepQ Learning on GPUs Using On...

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22nd International conference on parallel and distributed Computing, Applications and Technologies (PDCAT 2021)

作者： Koester, Marcel Gross, Julian Krueger, Antonio German Res Ctr Artificial Intelligence DFKI Saarland Informat CampusCampus D3-2 D-66123 Saarbrucken Germany

ISBN: (纸本)9783030967727;9783030967710

The current trend of using artificial neural networks to solve computationally intensive problems is omnipresent. In this scope, DeepQ learning is a common choice for agent-based problems. DeepQ combines the concept of Q-Learning with (deep) neural networks to learn different Q-values/matrices based on environmental conditions. Unfortunately, DeepQ learning requires hundreds of thousands of iterations/Q-samples that must be generated and learned for large-scale problems. Gathering data sets for such challenging tasks is extremely time consuming and requires large data-storage containers. Consequently, a common solution is the automatic generation of input samples for agent-based DeepQ networks. However, a usual workflow is to create the samples separately from the training process in either a (set of) pre-processing step(s) or interleaved with the training process. This requires the input Q-samples to be materialized in order to be fed into the training step of the attached neural network. In this paper, we propose a new GPU-focussed method for on-the-fly generation of training samples tightly coupled with the training process itself. This allows us to skip the materialization process of all samples (e.g. avoid dumping them disk), as they are (re)constructed when needed. Our method significantly outperforms usual workflows that generate the input samples on the CPU in terms of runtime performance and memory/storage consumption.

关键词： Massively-parallel processing Neural networks Q-learning Graphics processing units GPUs State construction

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Research on raw speech isolated word recognition based on Sincnet-CNN model 3

Research on raw speech isolated word recognition based on Si...

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3rd International conference on Information Science, parallel and distributed Systems, ISPDS 2022

作者： Hu, Gao-Yang Zeng, Qing-Ning Long, Chao Geng, Dian-Qiang Guilin University of Electronic Technology School of Information and Communication Guilin China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

In order to effectively speed up the model training time, reduce the model training parameters and improve the accuracy of raw speech isolated word recognition. An interpretable convolutional filter structure (sincnet) combined with convolutional neural network (CNN) is proposed for the task of raw speech isolated word recognition. On the premise of ensuring the speech recognition rate, the model structure becomes lightweight and the computational complexity is reduced. The experimental results show that compared with the traditional neural network model, the proposed model can effectively improve the performance of raw speech isolated word recognition. © 2022 IEEE.

关键词： Convolution

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Indian Currency Recognition:A Lightweight Transfer Learning Approach for Visually Impaired 7

Indian Currency Recognition:A Lightweight Transfer Learning ...

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7th International conference on parallel, distributed and Grid Computing, PDGC 2022

作者： Saini, Poonam Goyal, Ankit Pokhrel, Bishwash Singh, Jaspreet Kumar, Neetish Punjab Engineering College Dept. of Computer Science and Engg. Chandigarh India

ISBN: (纸本)9781665454018

The knowledge of artificial intelligence, especially, machine learning has been widely used to handle various real-world applications and related societal challenges. One of the crucial issues faced by visually impaired people is to correctly recognize paper currency and its denomination easily. The paper proposes a convolutional neural network-based currency recognition system applied to the Indian Currency. The basic features include data pre-processing to filter out the old currency notes and incorrect labels, followed by a pre-trained model selection. The proposed model is trained using transfer learning, and afterward, a custom layer is added to recognize currency notes. Further, a fully connected customized neural network architecture has been designed. The model undergoes fine tuning of essential hyperparameters. The proposed model classifies Indian banknotes with more reliable accuracy (99.5%), which is even flexible to different image orientations (flipped, folded, out of frame, etc.) as well as empty backgrounds for a real-time application. © 2022 IEEE.

关键词： network architecture

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TEngine: A Native distributed Table Storage Engine

TEngine: A Native Distributed Table Storage Engine

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International conference on Data Engineering

作者： Xiaopeng Fan Song Yan Yuchen Huang Chuliang Weng School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

With the rapid development of storage and network technology, emerging high-performance hardware is being widely applied to the distributed storage cluster. However, existing distributed storage systems employing multi-layer abstractions to provide table data services result in leaving high-speed hardware under-exploited. In this paper, we propose TEngine, a native distributed table storage engine designed for NVMe SSD and RDMA. The key is that TEngine removes the file abstraction to construct table structures on the device directly. For metadata service, TEngine designs a decoupled single metadata server, reducing distributed coordination, easing the burden on the metadata node, and enabling localized data node access. For data service, TEngine optimizes the parallel processing capability of NVMe devices by integrating upper-level multi-thread parallel operations with lower-level NVMe devices' parallel I/O processing. Moreover, TEngine introduces a periodic pull-based data synchronization approach to transform data pushing into periodic data pulling, which offloads the synchronization burden from the leader to the followers. The experimental results show that TEngine outperforms state-of-the-art distributed storage systems using the same hardware environment.

关键词： distributed databases Transforms Metadata parallel processing Data engineering Hardware Synchronization

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STGAT: Spatial-Temporal Graph Attention networks for Traffic Flow Prediction 29

STGAT: Spatial-Temporal Graph Attention Networks for Traffic...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Ruan, Chang Tan, Xianchao Liao, Zhuofan Kuang, Lidan Li, Ping Changsha University of Science & Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798350330717

Accurate traffic flow prediction is of great importance in Intelligent Transportation System (ITS) for improving traffic efficiency, reducing congestion and so on. However, due to the complex spatial and temporal dependencies, achieving the accurate prediction is challenging. Traditional attention-based networks for traffic flow prediction typically use sine and cosine functions to do position encoding, which fail to capture the spatial and temporal dependencies and do not contain the graph structure information. In this paper, we propose a novel model named Spatial-Temporal Graph ATtention networks (STGAT), which leverages structure-aware self-attention mechanism to predict future traffic flow. The model introduces temporal multi-head self-attention modules, and designs spatial multi-head graph self-attention modules with structure-aware graph filters to extract more temporal and spatial information. Besides, our model adopts temporal and spatial position embedding layers to capture the spatial-temporal dependencies in traffic flow data. Experimental results show that STGAT shows better prediction performance than the state-of-art models on three real-world datasets PEMS04, PEMS07 and PEMS08. For example, we observe up to 10.38% improvement in terms of Mean Absolute Percentage Error (MAPE) compared with the well-known model ASTGNN. © 2023 IEEE.

关键词： graph attention network self-attention spatial-temporal Traffic flow prediction

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Cross-view Self-Supervised Learning with Graph Convolutional networks for Group Recommendation 29

Cross-view Self-Supervised Learning with Graph Convolutional...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Liu, Dan Wang, Nan Li, Jinbao Heilongjiang University College of Computer Science and Technology Harbin China Qilu University of Technology Shandong Artificial Intelligence Institute Jinan China

ISBN: (纸本)9798350330717

With the development of social networks, people increasingly tend to form groups to participate in many daily activities, such as family dinners and company reunions, so the focus of recommendation systems has changed from personalization for individual users to consensus for groups. This process of recommending items to groups is called group recommendation. Most existing methods aggregate the individual preferences of group members using heuristic or attention mechanism-based methods to obtain group preferences. However, these methods face two problems. Firstly, they only model the user preferences of individual groups, ignoring the complicated high-order interactions that exist within and outside the group. Secondly, the sparsity of group-item interactions makes group recommendations suffer from data sparsity. We propose a cross-view self-supervised learning with graph convolutional networks for group recommendation to address the above limitations. Specifically, we design it to obtain better group preferences, including hypergraph propagating module view and bipartite graph propagating module view. Then, by applying cross-view self-supervised learning, the relationships between the two different views are established, and the information between different views can be complementary, thereby enhancing each other. Extensive experiments on two public datasets have demonstrated that our method outperforms the state-of-the-art baseline. © 2023 IEEE.

关键词： Group Recommendation Hypergraph Convolution network Self-Supervised Learning User Behavior Modeling

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