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检索条件"任意字段=Seventh International Symposium on Parallel Architectures, Algorithms and Programming"

共 513 条记录，以下是41-50 订阅

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An Efficient Ambient Noise Cross-Correlation Algorithm on Heterogeneous CPU-GPU Cluster

An Efficient Ambient Noise Cross-Correlation Algorithm on He...

引用

international symposium on parallel architectures, algorithms and programming (PAAP)

作者： Chao Wu Xing Tan Huikun Li Guangzhong Sun Network and Information Center Supercomputing Center University of Science and Technology of China Hefei China School of Earth and Space Sciences University of Science and Technology of China Hefei China School of Software Engineering University of Science and Technology of China Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781665452199

Calculation of noise cross-correlation functions (NCF) plays a vital role in ambient noise seismology. However high computation and storage requirements of NCF become huge obstacles in development of ambient noise seismology. Nowadays heterogeneous clusters show great impact in scientific computing. In this paper, we propose a parallel NCF algorithm based on heterogeneous cluster. Firstly, input SAC files are partitioned by dimension of dates and distributed to different nodes of heterogeneous cluster. Secondly, during NCF computation in each node, workloads are divided by station pairs, and calculated in different graphics processing units (GPUs) embedded in computing node. Finally, all NCF results of different dates are gathered to one computing node and stacked using all central processing units (CPUs) of that node. Experimental results demonstrate that the parallel NCF computing can be accomplished within much less time than that of the CPU counterpart, and the speedup remains almost linear when increasing number of computing nodes.

关键词： Correlation Scientific computing Seismology Clustering algorithms Graphics processing units programming parallel processing

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parallel Accelerating Ultra-Long Read Alignment by Vertical Partitioning Data

Parallel Accelerating Ultra-Long Read Alignment by Vertical ...

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international symposium on parallel architectures, algorithms and programming (PAAP)

作者： Deng Pan Cheng Zhong Danyang Chen Jinxiong Zhang Feng Yang School of Computer Electronics and Information Key Laboratory of Parallel Distributed Computing Technology in Guangxi Universities Guangxi University Nanning Guangxi China School of Computer Electronics and Information Key Laboratory of Parallel Distributed Computing Guangxi University Nanning Guangxi China

ISBN: (纸本)9781665452199

The alignment between sequencing reads and genome is a basic work in biological big data analysis. Each read of the third generation sequencing data is getting longer, and the data size is getting larger. To effectively solve the ultra-long read alignment problem with high requirements for computing and memory capacity, a strategy for vertical partitioning ultra-long reads on hybrid CPU/GPU cluster is proposed, and a heap data structure is used to filter the local aligned results in all computing nodes of the parallel cluster system according to the alignment score to reduce the data transmission size. The methods for early termination and parallel merging-splicing are used to accelerate splicing local aligned results. The local aligned results among all computing nodes are collected and extended to obtain the final alignment results. The experimental results on datasets of simulated and real ultra-long reads show that the proposed parallel alignment algorithm can obtain high alignment accuracy, sensitivity and base-level sensitivity as a whole, and accelerate completing alignment between ultra-long reads and reference genome.

关键词： Sequential analysis Sensitivity Splicing Merging Memory management Genomics programming

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PyPIM: Integrating Digital Processing-in-Memory from Microarchitectural Design to Python Tensors

PyPIM: Integrating Digital Processing-in-Memory from Microar...

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IEEE/ACM international symposium on Microarchitecture (MICRO)

作者： Orian Leitersdorf Ronny Ronen Shahar Kvatinsky Viterbi Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Haifa Israel

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Digital processing-in-memory (PIM) architectures mitigate the memory wall problem by facilitating parallel bitwise operations directly within the memory. Recent works have demonstrated their algorithmic potential for accelerating data-intensive applications; however, there remains a significant gap in the programming model and microarchitectural design. This is further exacerbated by aspects unique to memristive PIM such as partitions and operations across both directions of the memory array. To address this gap, this paper provides an end-to-end architectural integration of digital memristive PIM from a high-level Python library for tensor operations (similar to NumPy and PyTorch) to the low-level microarchitectural design. We begin by proposing an efficient microarchitecture and instruction set architecture (ISA) that bridge the gap between the low-level control periphery and an abstraction of PIM parallelism. We subsequently propose a PIM development library that converts high-level Python to ISA instructions and a PIM driver that translates ISA instructions into PIM micro-operations. We evaluate PyPIM via a cycle-accurate simulator on a wide variety of benchmarks that both demonstrate the versatility of the Python library and the performance compared to theoretical PIM bounds. Overall, PyPIM drastically simplifies the development of PIM applications and enables the conversion of existing tensor-oriented Python programs to PIM with ease.

关键词： Microarchitecture Tensors Instruction sets Computational modeling Memory architecture programming parallel processing Libraries Partitioning algorithms Python

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A Dense Tensor Accelerator with Data Exchange Mesh for DNN and Vision Workloads 53

A Dense Tensor Accelerator with Data Exchange Mesh for DNN a...

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IEEE international symposium on Circuits and Systems (IEEE ISCAS)

作者： Lin, Yu-Sheng Chen, Wei-Chao Yang, Chia-Lin Chien, Shao-Yi Inventec Corp Taipei Taiwan Natl Taiwan Univ Taipei Taiwan

ISBN: (纸本)9781728192017

We propose a dense tensor accelerator called VectorMesh, a scalable, memory-efficient architecture that can support a wide variety of DNN and computer vision workloads. Its building block is a tile execution unit (TEU), which includes dozens of processing elements (PEs) and SRAM buffers connected through a butterfly network. A mesh of FIFOs between the TEUs facilitates data exchange between tiles and promote local data to global visibility. Our design performs better according to the roofline model for CNN, GEMM, and spatial matching algorithms compared to state-of-the-art architectures. It can reduce global buffer and DRAM fetches by 2-22 times and up to 5 times, respectively.

关键词： Neural network hardware vector processors parallel programming

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Deep Just-In-Time Consistent Comment Update via Source Code Changes

Deep Just-In-Time Consistent Comment Update via Source Code ...

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international symposium on parallel architectures, algorithms and programming (PAAP)

作者： Shikai Guo Xihui Xu Hui Li Rong Chen The College of Information Science and Technology Dalian Maritime University Dalian China

ISBN: (纸本)9781665452199

During software development and maintenance, code comments are often missing, inadequate, or they do not match the actual code content. In response to this problem, the research community has proposed a method for updating natural language comments based on code changes. However, there are two major limitations of this method that must be addressed: the long-term and non-temporal dependencies in the source code. To address these limitations, we propose a new model called code semantic learning–comment update (CSL2CU). The code semantic learning component of CSL2CU uses a self-attention mechanism and a positional encoding mechanism. It also uses a relative positional representation to model pairwise relationships between source code tags, thereby improving its ability to capture long-term dependencies and non-temporal dependencies of source code tagging ability. The comment-update component of CSL2CU is used to generate new comments based on old comments and code editing. The experimental results show that the CSL2CU model outperforms the three baselines used in exact match, BLEU, METEOR, and SARI.

关键词： Codes Source coding Semantics Natural languages Tagging programming Maintenance engineering

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MCVAE: Multi-channel Variational Autoencoder for Anomaly Detection

MCVAE: Multi-channel Variational Autoencoder for Anomaly Det...

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international symposium on parallel architectures, algorithms and programming (PAAP)

作者： Wanzhen Zhang Liheng Xu Zhihui Yu Zikai Zhang Tonglai Liu Shuangyin Liu Engineering Trainning Center Guilin University of Aerospace Technology (College of Information Science and Technology Zhongkai University of Agriculture and Engineering) Guilin China College of Information Science and Technology Zhongkai University of Agriculture and Engineering Guangzhou China State Key Lab of Rail Traffic Control & Safety Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665452199

Unsupervised anomaly detection is a very important problem due to its wide applications in many systems such as the network communication system, the Internet of Things, and the ICS system. Interpretable modeling of heterogeneous data channels is also essential in anomaly detection, due to the intrinsic multi-modality in multi-channel multi-dimension data. Some existing works use variational autoencoder (VAE) for anomaly detection with a single Gaussian distribution model. However, these VAE can not learn the complex distribution between features, and hence cannot make accurate detections. To tackle this challenge, in this paper, we propose a multi-channel VAE model to jointly account for latent relationships across multiple channels. Moreover, we explore the training loss function combining the reconstruction loss of each channel. We also explore the test loss combining the reconstruction probability and reconstruction ratio between global channel information and each channel information. The proposed detector reports an anomaly when the test loss is below a certain threshold. We conduct extensive simulations on a real world dataset and find that our proposed scheme outperforms the state-of-the-art anomaly detection schemes compared with existing methods.

关键词： Training Detectors programming Gaussian distribution Feature extraction parallel architectures Internet of Things

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Do Not Have Enough Data? An Easy Data Augmentation for Code Summarization

Do Not Have Enough Data? An Easy Data Augmentation for Code ...

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international symposium on parallel architectures, algorithms and programming (PAAP)

作者： Zixuan Song Xiuwei Shang Mengxuan Li Rong Chen Hui Li Shikai Guo Dalian Maritime University The College of Information Science and Technology Dalian China

ISBN: (纸本)9781665452199

Code comments improve the readability and intelligibility of codes, which can help developers understand programs and improve the efficiency of the software maintenance and evolution process. Unfortunately, code comments are often mismatched, missing, or outdated in software projects, which negatively affects the efficiency of developers to infer the functionality from source code and affect the efficiency of software maintenance and evolution. To solve this problem, many source code summarization algorithms have been proposed. However, these methods usually try to collect a large data set which contains the mapping between code comments and source code to train models. Hence, the effectiveness of the models often rely on the quality of the training data. There are two limitations for the training sets: the insufficient data collection limitation (i.e., generate a large amount of noises-free training data) and data distribution bias limitation (i.e., generate training data for infrequently used methods). To address this issues, we have proposed a data augmentation method for code comments, named CDA-CS. Extensive experiments on Java and Python projects collected from GitHub are conducted to evaluate the performance of CDA-CS. Training models on the augmented dataset, the state-of-the-art algorithms can easily get a further 1.37% to 2.24% improvement in terms of different evaluation metrics (i.e., BLEU-4, METEOR, ROUGH_L) with no additional cost.

关键词： Training Software maintenance Codes Source coding Software algorithms Training data programming

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A Multi-object Detection Sampling Algorithm For Large Scenes

A Multi-object Detection Sampling Algorithm For Large Scenes

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international symposium on parallel architectures, algorithms and programming (PAAP)

作者： Liang Jin Xiaochuan Li Baoyu Fan Zhenhua Guo Ruidong Li Li Wang Yanwei Wang Yaqian Zhao Rengang Li Inspur Electronic Information Industry Co.Ltd. Jinan China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Center Co.Ltd. Jinan China

ISBN: (纸本)9781665452199

Multi-object detection in large scenes aims to find objects in images, which usually contain more than one billion pixels. Based on the concept of dividing and conquering, the state-of-the-art (SOTA) methods slice the super-resolution image into patches first and then lower the image solution to detect objects later. The advantage of this method is that it can adapt quickly to regular detection algorithms. However, a set of parameters needs to be set manually, such as the size of sliding windows and overlap, which is quite hard to fit all scenarios. It may result in a loss of samples located at the boundary of the sliding window and the oversampling of inefficient samples that appear within the overlap. In this paper, we propose a object-oriented image sampling algorithm based on anchor boxes during training and multi-scale pyramids during inference. Inspired by the mature object detection baseline Scale-YOLOv4, we present more tricks to fit large scenes. The accuracy can reach 66%, which is 24 points higher than the CascadeRCNN model of the official backbone network ResNet50. Finally, we have won first place in the PANDA object detection tracking using this method.

关键词： Training Object oriented modeling Superresolution Object detection programming Image sampling Inference algorithms

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Cluster-based Federated Learning Framework for Intrusion Detection

Cluster-based Federated Learning Framework for Intrusion Det...

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international symposium on parallel architectures, algorithms and programming (PAAP)

作者： Luxin Cai Naiyue Chen Yuanmeng Wei Huaping Chen Yidong Li School of Computer and Information Technology Beijing Jiaotong University Beijing China Qi’anxin Technology Group Co. Ltd.. Beijing China

ISBN: (纸本)9781665452199

With the rapid development of Industrial Internet, the network intrusion detection has become particularly important. In the Industrial Internet, large-scale data is distributed in the edge nodes caused the joint analysis of network intrusion detection at each edge node has become necessary. Federated learning structure can avoid data out of local nodes to protect user privacy data. However, the data distribution is different for each edge nodes, which limits the effectiveness of federated learning models. We focus on the non-IID data features and propose a new cluster-based federated learning framework for network intrusion detection. In this method, we cluster clients into different communities by data labels, which the clients contain the similar proportion of data labels in the same community. Based on the clustering results, we decompose federated learning model aggregation into cluster aggregation and global aggregation by leveraging similarities both within and between clusters. We conduct extensive experiments based on UNSW_NB15 dataset. The results show that our method has better performance than FedAvg and FedProx. It can work well in scenarios with different distributions of data samples while ensuring data security and privacy protection.

关键词： Data privacy Federated learning Image edge detection Data security Network intrusion detection Distributed databases programming

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Multi-Modal Virtual-Real Fusion based Transformer for Collaborative Perception

Multi-Modal Virtual-Real Fusion based Transformer for Collab...

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international symposium on parallel architectures, algorithms and programming (PAAP)

作者： Hui Zhang Guiyang Luo Yuanzhouhan Cao Yi Jin Yidong Li School of Computer and Information Technology Beijing Jiaotong University Beijing China the State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665452199

Automobile intelligence and networking have become the inevitable trend in the future development of the automotive industry. Existing intelligent and connected vehicles rely on single-agent intelligence to perform the basic perception, which is still weak in dealing with the problem of accurate recognition and positioning in complex traffic scenes such as small and far away objects. To tackle this issue, we propose a multi-model virtual-real fusion Transformer for collaborative perception. Specifically, to possess the complementary information from both RGB images and LiDAR point clouds, we propose the multi-model virtual-real fusion (MVRF) method, which generates virtual points and compensates for the lack of point information on sparse locations. Furthermore, the heterogeneous graph attention network (HGAN) is constructed to capture the inter-agent interaction and adaptively incorporate multiple agents’ features. The HGAN contains a series of encoder layers, each of which has a heterogeneous inter-agent attention module and a multi-scale self-attention module, which motivates to learn different relationships based on various agents’ types and simultaneously capture the global and local spatial attention. Extensive experiments demonstrate that the proposed method gains superior performance as compared with state-of-the-art methods.

关键词： Point cloud compression Industries Connected vehicles Laser radar Collaboration programming Transformers

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