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Domain Decomposition Preconditioners for Unstructured Network Problems in parallel Vector architectures 21

Domain Decomposition Preconditioners for Unstructured Networ...

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50th international Conference on parallel Processing (ICPP)

作者： Maldonado, Daniel Adrian Schanen, Michel Pacaud, Francois Anitescu, Mihai Argonne Natl Lab Lemont IL 60439 USA

ISBN: (纸本)9781450384414

In this paper we present our experience implementing domain decomposition preconditioners on vector architectures. In particular, we will focus on the solution of unstructured network equations arising from electrical power systems by preconditioning iterative algorithms with the Additive Schwarz Method (ASM). The implementation will be carried out using the Julia programming language, which allows for easy prototyping and interfacing with GPU architectures thanks to its multiple dispatch features. In our experiments, we will show the trade-off between device throughput and convergence of the iterative algorithm as the size of the domain varies, and determine optimal fronts of computational performance.

关键词： Iterative methods

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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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Combining Static and Dynamic Analysis to Query Characteristics of HPC Applications

Combining Static and Dynamic Analysis to Query Characteristi...

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35th IEEE international parallel and Distributed Processing symposium (IPDPS)

作者： Welch, Aaron Hernandez, Oscar Chapman, Barbara Univ Houston Houston TX 77004 USA Oak Ridge Natl Lab Oak Ridge TN USA SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9781665435772

Emerging HPC platforms are becoming more difficult to program as a result of systems with different node architectures, some with a small number of "fat" heterogenous nodes (consisting of multiple accelerators) and others with a large number of "thin" homogenous nodes consisting of multi-core CPUs connected with high speed interconnects. New programming models are emerging to address performance portability of the applications as well as a set of scientific libraries that applications can use to exploit these architectures efficiently. To port applications to new architectures, developers need information about their source code characteristics including static and dynamic (e.g. performance) information to refactor the code, understand their data and code structure, and library usage as well as program information to direct their optimisation efforts and make key decisions. In this paper, we describe a tool that combines compiler and profiler information to query program characteristics in a given programming environment. Static and dynamic data about applications is collected and stored together in an SQL database that can be later queried to study application characteristics and patterns. We will demonstrate the capabilities of this tool with an application-driven case study that aims at understanding application code and its use of scientific libraries via a real world example from the molecular simulation application CP2K.

关键词： Distributed processing Program processors Databases Conferences Tools programming Libraries

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Performance Improvements of parallel Applications thanks to MPI-4.0 Hints 34

Performance Improvements of Parallel Applications thanks to ...

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34th IEEE international symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Moraru, Maxim Roussel, Adrien Taboada, Hugo Jaillet, Christophe Perache, Marc Krajecki, Michael Univ Reims LICIIS F-51097 Reims France CEA DAM DIF LRC DIGIT F-91297 Arpajon France Univ Paris Saclay CEA Lab Informat Haute Peiformance Calcul & Simulat Bruyeres Le Chatel France CEA DAM DIF F-91297 Arpajon France Univ Reims LRC DIGIT LICIIS F-51097 Reims France

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

HPC systems have experienced significant growth over the past years, with modern machines having hundreds of thousands of nodes. Message Passing Interface (MPI) is the de facto standard for distributed computing on these architectures. On the MPI critical path, the message-matching process is one of the most time-consuming operations. In this process, searching for a specific request in a message queue represents a significant part of the communication latency. So far, no miracle algorithm performs well in all cases. This paper explores potential matching specializations thanks to hints introduced in the latest MPI 4.0 standard. We propose a hash-table-based algorithm that performs constant time message-matching for no wildcard requests. This approach is suitable for intensive point-to-point communication phases in many applications (more than 50% of CORAL benchmarks). We demonstrate that our approach can improve the overall execution time of real HPC applications by up to 25%. Also, we analyze the limitations of our method and propose a strategy for identifying the most suitable algorithm for a given application. Indeed, we apply machine learning techniques for classifying applications depending on their message pattern characteristics.

关键词： HPC Distributed programming MPI Matching MPI 4.0 Sessions

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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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Structuring Meaningful Code Changes in Developer Community

Structuring Meaningful Code Changes in Developer Community

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

作者： Mengxuan Li Shikai Guo Xin Ge Hui Li Rong Chen The College of Information Science and Technology Dalian Maritime University Dalian China

ISBN: (纸本)9781665452199

The rapid development of Open-Source Software (OSS) has resulted in a significant demand for code changes to maintain OSS. Symptoms of poor design and implementation choices in code changes often occur, thus heavily hindering code reviewers to verify correctness and soundness of code changes. Researchers have investigated how to learn meaningful code changes to assist developers in anticipating changes that code reviewers may suggest for the submitted code. However, there are two main limitations to be addressed, including the limitation of long-range dependencies of the source code and the missing syntactic structural information of the source code. To solve these limitations, we propose a novel method named GTCT. GTCT comprises two components: code graph embedding and code transformation learning. To address the missing syntactic structural information, we encoding the source code into a code graph structure from the lexical and syntactic representations of the source code. Subsequently, we uses the multi-head attention mechanism and positional encoding mechanism to address the long-range dependencies limitation. Extensive experiments are conducted to evaluate the performance of GTCT by both quantitative and qualitative analyses. For the quantitative analysis, GTCT relatively outperforms the baseline on six datasets by 210%, 342.86%, 135%, 29.41%, 109.09%, and 91.67% in terms of perfect prediction. Meanwhile, the qualitative analysis shows that each type of code change by GTCT outperforms that of the baseline method in terms of bug fixed, refactoring code, and others’ taxonomy of code changes.

关键词： Codes Statistical analysis Source coding Taxonomy Software algorithms Syntactics programming

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Generating High Quality Titles in StackOverflow via Data Denoising Method

Generating High Quality Titles in StackOverflow via Data Den...

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

作者： Shikai Guo Bowen Ping Zixuan Song Hui Li Rong Chen school of information and technology Dalian Maritime University Dalian China

ISBN: (纸本)9781665452199

StackOverflow is one of the most popular question-and-answer platforms on the internet and whether posts on StackOverflow will be answered largely depends on their titles’ quality. Based on recurrent neural networks (RNN) or transformers, previous studies have attempted to use real posts from StackOverflow to generate better titles. However, the challenge of noise in existing data has been ignored, leading models can’t generate higher quality titles. To address this issue, we propose the K-clusters confidence learning for code titles (KCL-CT) model, which contains code clustering and confident learning (CL) denoising components. Specifically, the code clustering component is used to capture the word order and semantic information in code and classify code into different functional categories. The CL denoising component receives the output from the code clustering component and employs a heuristic method based on a confidence threshold to prune raw datasets. We conducted experiments based on Java, Python, JavaScript, SQL and C# datasets, the results of which indicated that in terms of the BLEU and ROUGE scores, the proposed KCL-CT model can outperform previous state-of-the-art models by 2.0%–11.1% and 2.5%–14.0%, respectively.

关键词： Java Codes Recurrent neural networks Noise reduction Semantics programming Transformers

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PNS Lock: A Portable NUMA-Aware Lock with a Standard Interface

PNS Lock: A Portable NUMA-Aware Lock with a Standard Interfa...

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parallel Computing and Distributed Systems (PCDS), international symposium on

作者： Brahmaiah Gandham Praveen Alapati Computer Science and Engineering Ecole Centrale School of Engineering Mahindra University Hyderabad India

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

In shared memory programming, there is a need for synchronization primitives that are suitable for modern hardware to achieve high performance and reduce contention. This paper introduces a novel lock with a standard interface, namely, the PNS Lock. It is a lock for Non-Uniform Memory Access (NUMA) architectures and uses one queue for each NUMA node. PNS lock uses atomic operation for lock handovers between NUMA nodes. This design helps to get better performance by minimizing cross-node communication and contention. PNS lock intelligently adapts to the underlying system topology. This paper discusses the design of a PNS lock and compares it's performance by considering the state-of-the-art synchronization objects. PNS lock also provides a standard interface and ensures seamless integration with existing multi-threaded applications. From the experimental evaluation, it is observed that PNS lock helps to enhance the performance and increases the cache hit rate. This synchronization primitive, PNS lock is a great tool for the developers in NUMA environments.

关键词： Memory architecture Handover programming parallel processing Data structures Topology Synchronization Servers Standards Optimization

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A hybrid strategy for parallel parking by combining geometric and optimization planners

A hybrid strategy for parallel parking by combining geometri...

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Artificial Intelligence and Intelligent Manufacturing (AIIM), international symposium on

作者： Bin Li Junfu Huang Yunfei Zhang Hao Chen China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

Path planning is a key function of automatic parking systems, especially for parallel parking in tight spaces. A qualified planner should compute quickly, ensure optimality, and avoid extra halfway stops. This paper proposes a hybrid strategy to make up the shortcomings of geometric path planners by optimizing the pose and heading angle at the first entry point into the parking space. Considering the number of maneuvers in the limited parking space, the computation of the entry point is described as a nonlinear programming problem, and solved by the genetic algorithm offline. Based on these, feasible paths for different parallel parking spaces can be directly provided online using the parametric form of circular arcs. The effectiveness of the hybrid strategy is validated by comparative tests in several parallel parking spaces of varying sizes, and the details about adjusting maneuvers in the parking space are discussed. Additionally, the traceability of the planned paths is verified by experimental tests.

关键词： Actuators Bandwidth programming Linear programming Path planning Planning Manufacturing Artificial intelligence Optimization Genetic algorithms

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