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Research on the Development and Application of Parallel programming Technology in Heterogeneous Systems

Journal of Physics: conference Series 2022年第1期2173卷

作者： Linghong Wu School of Computer and Information Engineering Nanchang Institute of Technology Nanchang Jiangxi 330044 China

In recent years, with the continuous improvement of high performance computing hardware platform,the application of high performance for a wide range of high-performance, traditional parallel programming environment tools cannot meet the growing demand for high-performance applications and the face of parallel software development environment in a relatively backward state. The heterogeneous system of the new coprocessor with a high performance and power ratio can have high performance computing needs to provide the task to accelerate. As a result, heterogeneous systems are becoming a new trend in high performance computing. However, with the introduction of the new coprocessor and the expansion of the system scale, heterogeneous systems are difficult to program the problem is also increasingly prominent. In view of this problem, this paper adopts the abstract technology to enhance the granularity of parallel programming, and designs a structured parallel programming prototype system which can achieve different levels of implicit parallelism

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CoFilter: A high-performance Switch-Accelerated Stateful Packet Filter for Bare-Metal Servers 28

CoFilter: A High-Performance Switch-Accelerated Stateful Pac...

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28th International conference on computer Communications and Networks (ICCCN)

作者： Cao, Jiamin Liu, Ying Zhou, Yu Sun, Chen Wang, Yangyang Bi, Jun Tsinghua Univ Inst Network Sci & Cyberspace Beijing Peoples R China

ISBN: (纸本)9781728118567

As one of the most critical cloud services, Baremetal Servers introduce stringent performance requirements on data center networks (DCN). Stateful packet filter is an integral DCN component of ensuring connection security for bare-metal servers. However, the off-the-shelf hardware-based and software-based stateful packet filters either are prohibitively costly for cloud DCNs or introduce significant performance bottlenecks. In this paper, we present CoFilter, which employs cheap programmable switches to accelerate the stateful packet filter for bare-metal servers. CoFilter consists of two key designs. First, to support complex stateful packet filtering logic in programmability-limited switching ASICs, CoFilter partitions the stateful packet filtering logic between programmable ASICs and switch CPU. Most packets are directly processed in switching ASICs to achieve high performance, while only a small number of packets go to switch CPU for connection tracking. Second, to track massive connections with constrained hardware memory, CoFilter employs hash to compress connection states and provides an efficient settlement for hash collisions. We build a prototype of CoFilter and evaluate it on the Tofino switch under various data center traffic traces with real-world flow distribution. The evaluation shows that CoFilter largely outperforms NetFilter, i.e., forwarding packets at line rate (13x throughput of NetFilter), keeping packet delay at 1us, and freeing a significant quantity of CPU cores. Furthermore, CoFilter presents great scalability and accommodates over ten million connections with only 16MB SRAM.

关键词： bare metal server stateful packet filter programmable switch

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Remote Sensing Image Fusion Based on Multi-morphological Convolutional Neural Network 3rd

Remote Sensing Image Fusion Based on Multi-morphological Con...

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3rd International conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Zhu, Meng Xu, Jindong Liu, Zhaowei School of Computer and Control Engineering Yantai University Yantai264000 China

ISBN: (纸本)9783030624620

In this paper, we propose a method for remote sensing image fusion based on multi-morphological convolutional neural network (MCNN). First, MCNN combines a local discrete cosine transform (LDCT) dictionary and a curvelet transform (CT) dictionary to form a decomposition dictionary. By adjusting the size of the threshold, the morphological component analysis (MCA) method is used to sparsely decompose the remote sensing image, the texture component and the cartoon component (segmented smooth component) are extracted from the remote sensing image, respectively. Secondly, combined with the convolutional neural network (CNN) that has achieved good results in the field of image processing. By inputting different forms of source images into the network, it outputs a fused image with end-to-end characteristics. The MCNN not only solves the problems that most existing models based on sparse algorithms are relatively complex and have high computational complexity, but also solves the problem that in the traditional deep learning fusion method, the original information in the source image is often lost when acquiring the image information. Compared with other commonly used remote sensing image fusion methods, experimental results show that MCNN has achieved good results in terms of objective evaluation indicators and subjective visual performance. © 2020, Springer Nature Switzerland AG.

关键词： Image fusion

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F3: Machine Learning Processors: From high performance Applications to Architectures and Benchmarking

F3: Machine Learning Processors: From High Performance Appli...

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IEEE International conference on Solid-State Circuits (ISSCC)

Provides an abstract of the tutorial presentation and may include a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings.

ISBN: (数字)9781728132051

ISBN: (纸本)9781728132068

关键词： Machine learning computer architecture program processors Hardware Training Benchmark testing

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An Accurate Indoor User Position Estimator for Multiple Anchor UWB Localization 11

An Accurate Indoor User Position Estimator for Multiple Anch...

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11th International conference on Information and Communication Technology Convergence, ICTC 2020

作者： Poulose, Alwin Emersic, Ziga Steven Eyobu, Odongo Seog Han, Dong Kyungpook National University School of Electronics Engineering Daegu Korea Republic of University of Ljubljana Faculty of Computer and Information Science Slovenia Makerere University School of Computing Informatics Technology Kampala Uganda

ISBN: (纸本)9781728167589

UWB-based positioning systems have been proven to provide a significant high level of ac-curacy hence offering a huge potential for a variety of indoor applications. However, the major challenges related to UWB localization are multipath effects, excess delay, clock drift, signal interferences and system computational time to estimate the user position. To compensate for these challenges, the UWB system uses multiple anchors in the experiment area and this gives accurate position results with minimum localization errors. However, the use of multiple anchors in the UWB system means processing large amounts of data in the system controller for localization, which leads to high computational time to estimate the current user position. To reduce the complexity of the UWB systems, we propose a position estimator for multiple anchor indoor localization, which uses the extended Kalman filter (EKF). The proposed UWB-EKF estimator was mathematically analysed and the simulation results were compared with classical localization algorithms considering the mean localization errors. In the simulation, three classical localization algorithms: linearized least square estimation (LLSE), weighted centroid estimation (WCE) and maximum likelihood estimation (MLE) were used for performance comparison. Thorough extensive simulation done in this study achieves results which demonstrate the effectiveness of the proposed UWB-EKF estimator for multiple anchor UWB indoor localization. © 2020 IEEE.

关键词： Maximum likelihood estimation

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You reap what you sow: Using Videos to Generate high Precision Object Proposals for Weakly-supervised Object Detection 32

You reap what you sow: Using Videos to Generate High Precisi...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Singh, Krishna Kumar Lee, Yong Jae Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781728132938

We propose a novel way of using videos to obtain high precision object proposals for weakly-supervised object detection. Existing weakly-supervised detection approaches use off-the-shelfproposal methods like edge boxes or selective search to obtain candidate boxes. These methods provide high recall but at the expense of thousands of noisy proposals. Thus, the entire burden of finding the few relevant object regions is left to the ensuing object mining step. To mitigate this issue, we focus instead on improving the precision of the initial candidate object proposals. Since we cannot rely on localization annotations, we turn to video and leverage motion cues to automatically estimate the extent of objects to train a Weakly-supervised Region Proposal Network (W-RPN). We use the W-RPN to generate high precision object proposals, which are in turn used to re-rank high recall proposals like edge boxes or selective search according to their spatial overlap. Our W-RPN proposals lead to significant improvement in performance for state-of-the-art weakly-supervised object detection approaches on PASCAL VOC 2007 and 2012.

关键词： Categorization Recognition: Detection Retrieval Video Analytics

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C-MAP: Improving the effectiveness of mapping method for CGRA by reducing NoC congestion 21

C-MAP: Improving the effectiveness of mapping method for CGR...

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21st IEEE International conference on high performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： An, Shuqian Zhang, Mingzhe Ye, Xiaochun Wang, Da Zhang, Hao Fan, Dongrui Tang, Zhimin University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture ICT CAS Beijing China

ISBN: (纸本)9781728120584

The Coarse-Grained Reconfigurable Architecture (CGRA) is considered as one of the most potential candidates for big data applications, which provides significant throughput improvement and high energy efficiency. Unlike the dynamic issue superscalar method in conventional processors, the CGRA architecture uses the static placement dynamic issue (SPDI) execution method in which the compiler decides how to map the instructions onto the distributed processing elements (PEs) and the PEs executes one instruction when the required data is ready. Since the dataflow of the program is determined in the logical view, an improper mapping of instructions may leads to more network congestion and hurts the performance. Furthermore, the exploration for most optimized mapping in CGRA is proved to a NPC problem and can hardly be achieved in limited time. In this paper, we propose a novel mapping algorithm named Congestion-MAP (C-MAP). C-Map improves the effectiveness of CGRA mapping in the perspective of reducing network congestion and enhancing the continuity of the data-flow. Furthermore, C-Map also accelerates the mapping optimization for CGRA by using network analysis method, which supports the fast comparison of mapping plan and parallel exploration. Additionally, with C-Map, we also analyze the impact of several key considerations in CGRA instruction mapping, such as NoC workload reduction and workload balance. The experiment result shows that C-Map improves the performance by 2.2× as a geometric mean. © 2019 IEEE.

关键词： Network-on-chip

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Hardware architecture design of HEVC entropy decoding

Hardware architecture design of HEVC entropy decoding

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

作者： Shiyu Wang Shengbing Zhang Jihe Wang Libo Chang Liangyou Feng Xiaoya Fan Engineering Research Center of Embedded System Integration Ministry of Education School of Computer Science Northwestern Polytechnical University Xian China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xian China

As the latest generation of digital video coding standard, HEVC has technically optimized multiple modules such as related frame prediction, block processing and entropy coding in the frequency coding and decoding framework. However, flexible and efficient coding algorithms make the amount of calculation in the video decoding and reconstruction process increase dramatically. The energy efficiency of traditional processors is limited, and the decoding calculation process is difficult to meet the current needs of ultra-high-definition video playback. For the most important and time-consuming bitstream analysis and entropy decoding part of the HEVC decoding process, a new hardware architecture strategy is provided, which can effectively improve the HEVC decoding performance. In this paper, the designed data access module, bitstream analysis and entropy decoding unit are simulated and verified. Build an FPGA verification platform, and use the main tier standard test sequence to simulate and verify the designed hardware acceleration architecture. The experimental results show that the hardware architecture of bitstream analysis and entropy decoding designed in this paper can reach the functions and performance indicators specified by the HEVC standard Level-4 main tier, and the bitstream analysis acceleration effect is good. This thesis aims at the current HEVC decoding calculation characteristics, and through the design of hardware architecture verification, it provides new solutions and hardware architecture ideas for subsequent HEVC encoding and decoding performance optimization.

关键词： Analytical models program processors Video sequences Bit rate Syntactics Prediction algorithms Entropy

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TripletRun: A dataflow runtime simulator and its performance model 21

TripletRun: A dataflow runtime simulator and its performance...

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作者： Lin, Han An, Hong Li, Mingfan Lin, Zeng Chen, Junshi Han, Wenting School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781728120584

Dataflow, as a representative fine-grained parallel model, has a great potential in heterogeneous computing thanks to its being good at representing dependencies and eliminating global synchronization. In this paper, we present TripletRun, a dataflow runtime simulator. It abstracts hardware features and software behaviors in a very general way. Moreover, it supports some state-of-the-art task scheduling algorithms and provides metrics for building performance model conveniently. TripletRun provides a new perspective for searching better dataflow program execution models and for fine-grained parallel task scheduling research on heterogeneous systems. Moreover, TripletRun also has the potential to be applied in actual systems. © 2019 IEEE.

关键词： Simulators

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Improving Academic performance Amongst First Years computer Science Students Through Goal-Setting

Improving Academic Performance Amongst First Years Computer ...

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Irish Signals and Systems conference (ISSC)

作者： Ryan Donovan Jamie Cotter Ruairi O'Reilly Computer Science Cork Institute of Technology Cork Ireland

ISBN: (数字)9781728194189

ISBN: (纸本)9781728194196

Academic performance across computer Science (CS) courses in the Republic of Ireland is underwhelming. CS undergraduates are statistically the most likely cohort in the country not to progress past year one of their studies. Insufficient motivation to pursue CS studies has been demonstrated to be a significant cause of poor CS academic performance. Goal-setting programs are an efficient, cost-effective, and student empowering way to boost motivation. Goal-setting is the formulation of a set of activities intended to motivate an individual to the desired goal state. This paper provides an experimental design for assessing the effectiveness of a written goal-setting program on academic performance concerning individual differences. Participants are randomly assigned either to the written goal-setting program or an active control task via an online platform. The goal-setting program requires participants to articulate both a desired future life and a feared future life. The program also requires participants to: identify goals and sub-goals across several domains (e.g. family, health, study); the benefits that achieving their goals would have for themselves for their connected group; the daily habits they could develop to make their ideal future more likely to occur. This study also investigates how personality (via a high-resolution personality model) and cognitive differences influence goal-setting effectiveness. Differences in both Semester 1 performance and the number of students who progress to Semester 2 are assessed between experimental groups. An ANCOVA analysis will assess whether the effectiveness of an experimental task varied based on individual differences in personality and/or cognitive ability.

关键词： Task analysis computer science STEM Psychology Negative feedback Writing Indexes

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