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Eunomia: Scaling Concurrent Index Structures Under Contention Using HTM

IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2018年第8期29卷 1837-1850页

作者： Zhang, Weihua Wang, Xin Ji, Shiyu Wei, Ziyun Wang, Zhaoguo Chen, Haibo Fudan Univ Software Sch Shanghai Key Lab Data Sci Shanghai 200433 Peoples R China Fudan Univ Parallel Proc Inst Shanghai 200433 Peoples R China NYU Dept Comp Sci 550 1St Ave New York NY 10003 USA Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai 200240 Peoples R China

Hardware transactional memory (HTM) is an emerging hardware feature. HTM simplifies the programming model of concurrent programs while preserving high and scalable performance. With the commercial availability of HTM-capable processors, HTM has recently been adopted to construct efficient concurrent index structures. However, with the expansion of data volume and user amount, data management systems have to process workloads exhibiting high contention;meanwhile, according to our experiments, the conventional HTM-base concurrent index structures fail to provide scalable performance under highly-contented workloads. Such performance pathology strictly constrains the usage of HTM on data management systems. In this paper, we first conduct a thorough analysis on HTM-based concurrent index structures, and uncover several reasons for excessive HTM aborts incurred by both false and true conflicts under contention. Based on the analysis, we advocate Eunomia, a design pattern for HTM-based concurrent index structure which contains several principles to improve HTM performance, including splitting HTM regions with version-based concurrency control to reduce HTM working sets, partitioned data layout to reduce false conflicts, proactively detecting and avoiding conflicting requests, and adaptive concurrency control strategy. To validate their effectiveness, we apply such design principles to construct a scalable concurrent B+ Tree and a skip list using HTM. Evaluation using key-value store and database benchmarks on a 20-core HTM-capable multi-core machine shows that Eunomia leads to substantial speedup under high contention, while incurring small overhead under low contention.

关键词： Hardware transactional memory concurrent index structure data conflicts

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High Performance Graph Analytics with Productivity on Hybrid CPU-GPU Platforms 2

High Performance Graph Analytics with Productivity on Hybrid...

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2nd International Conference on High Performance Compilation, Computing and Communications (HP3C)

作者： Yang, Haoduo Su, Huayou Lan, Qiang Wen, Mei Zhang, Chunyuan Natl Univ Def Technol Dept Comp Changsha 410000 Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha 410000 Hunan Peoples R China

ISBN: (纸本)9781450363372

In recent years, the rapid-growing scales of graphs have sparked a lot of parallel graph analysis frameworks to leverage the massive hardware resources on CPUs or GPUs. Existing CPU implementations are time-consuming, while GPU implementations are restricted by the memory space and the complexity of programming. In this paper, we present a high performance hybrid CPU-GPU parallel graph analytics framework with good productivity based on GraphMat. We map vertex programs to generalized sparse matrix vector multiplication on GPUs to deliver high performance, and propose a high-level abstraction for developers to implement various graph algorithms with relatively little efforts. Meanwhile, several optimizations have been adopted for reducing the communication cost and leveraging hardware resources, especially the memory hierarchy. We evaluate the proposed framework on three graph primitives (PageRank, BFS and SSSP) with large-scale graphs. The experimental results show that, our implementation achieves an average speedup of 7.0X than GraphMat on two 6-core Intel Xeon CPUs. It also has the capability to process larger datasets but achieves comparable performance than MapGraph, a state-of-the-art GPU-based framework.

关键词： parallel Computing Graph Analytics Hybrid CPU-GPU

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Towards a Multi-array Architecture for Accelerating Large-scale Matrix Multiplication on FPGAs

Towards a Multi-array Architecture for Accelerating Large-sc...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Shen, Junzhong Qiao, Yuran Huang, You Wen, Mei Zhang, Chunyuan Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781538648810

Large-scale floating-point matrix multiplication is a fundamental kernel in many scientific and engineering applications. Most existing work only focus on accelerating matrix multiplication on FPGA by adopting a linear systolic array. This paper towards the extension of this architecture by proposing a scalable and highly configurable multi-array architecture. In addition, we propose a work-stealing scheme to ensure the equality in the workload partition among multiple linear arrays. Furthermore, an analytical model is developed to determine the optimal design parameters. Experiments on a real-life convolutional neural network (CNN) show that we can obtain the optimal extension of the linear array architecture.

关键词： Arrays Silicon Bandwidth Task analysis Memory management Multiplexing

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HPGA: A High-Performance Graph Analytics Framework on the GPU

HPGA: A High-Performance Graph Analytics Framework on the GP...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Yang, Haoduo Su, Huayou Wen, Mei Zhang, Chunyuan Natl Univ Def Technol Dept Comp Changsha 410000 Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha 410000 Hunan Peoples R China

ISBN: (纸本)9781538657386

In recent years, the rapidly growing use of graphs has sparked parallel graph analytics frameworks for leveraging the massive hardware resources, specifically graphics processing units (GPUs). However, the issues of the unpredictable control flows, memory divergence, and the complexity of programming have restricted high-level GPU graph libraries. In this work, we present HPGA, a high performance parallel graph analytics framework targeting the GPU. HPGA implements an abstraction which maps vertex programs to generalized sparse matrix operations on GPUs for delivering high performance. HPGA incorporates high-performance GPU computing primitives and optimization strategies with a high-level programming model. We evaluate the performance of HPGA for three graph primitives (BFS, SSSP, PageRank) with large-scale datasets. The experimental results show that HPGA matches or even exceeds the performance of MapGraph and nvGRAPH, two state-of-the-art GPU graph libraries.

关键词： Graph Analytics High-performance Computing GPU

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General audio tagging with ensembling convolutional neural networks and statistical features

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JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2019年第6期145卷 EL521-EL527页

作者： Xu, Kele Zhu, Boqing Kong, Qiuqiang Mi, Haibo Ding, Bo Wang, Dezhi Wang, Huaimin Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Univ Surrey CVSSP Guildford GU2 7XH Surrey England Natl Univ Def Technol Coll Meteorol & Oceanog Changsha Hunan Peoples R China

Audio tagging aims to infer descriptive labels from audio clips and it is challenging due to the limited size of data and noisy labels. The solution to the tagging task is described in this paper. The main contributions include the following: an ensemble learning framework is applied to ensemble statistical features and the outputs from the deep classifiers, with the goal to utilize complementary information. Moreover, a sample re-weight strategy is employed to address the noisy label problem within the framework. The approach achieves a mean average precision of 0.958, outperforming the baseline system with a large margin.

关键词： Signal processing Artificial intelligence Spectrograms Regression analysis Decision theory Machine learning Programming languages Statistical mechanics models Acoustics

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Adaptive Differential Evolution Based on Successful Experience Information

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IEEE ACCESS 2020年 8卷 164611-164636页

作者： Cheng, Lianzheng Wang, Yun Wang, Chao Mohamed, Ali Wagdy Xiao, Tiaojie Chinese Acad Sci Inst Geochem State Key Lab Ore Deposit Geochem Guiyang 550081 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China China Univ Geosci Sch Geophys & Informat Technol Beijing 100083 Peoples R China Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Nile Univ Sch Engn & Appl Sci Wireless Intelligent Networks Ctr WINC Giza 16453 Egypt Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China

As a powerful optimization algorithm for solving nonlinear, complex and tough global optimization problems, differential evolution (DE) has been widely applied in various science and engineering fields. In this article, considering that the evolution direction of each individual is not fully exploited to guide the search process in most DE algorithms, a new DE variant (named ADEwSE), which incorporates the successful experience of evolved individuals into classic "current-to-pbest/1" mutation strategy to reduce the randomness of search direction, is proposed. Moreover, crossover matrix sorting scheme based on real crossover rate, opposition learning of crossover rate and adaptive adjustment of top p% values are combined with the new mutation strategy to improve the global search ability. In addition, to improve the searching ability of ADEwSE further, an ADEwSE variant by introducing the linear reduction of population size is proposed. In order to verify and analyze the performance of ADEwSE, numerical experiments on a set of 29 test problems from CEC2017 benchmark for 30, 50 and 100 dimensions are executed. And the experimental results are compared with that of 21 state-of-art DE-based algorithms. Comparative analysis indicates that the ADEwSE and its improved version are competitive with these state-of-art DE variants in terms of solution quality obtained.

关键词： Sociology Statistics Optimization Sorting Convergence Geochemistry Indexes Differential evolution successful experience opposition learning mutation strategy

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Energy efficient computation offloading for nonorthogonal multiple access assisted mobile edge computing with energy harvesting devices

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COMPUTER NETWORKS 2019年第0期164卷 106890-000页

作者： Li, Chunlin Tang, Jianhang Zhang, Yang Yan, Xin Luo, Youlong Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430063 Hubei Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Hunan Peoples R China Univ Sci & Technol China Anhui Prov Key Lab Big Data Anal & Applicat Hefei Anhui Peoples R China

The mobile edge computing (MEC) is combined with wireless energy transfer (WET) to enhance the performance of wireless devices in Internet of Things (IoT) systems, which is called wireless powered mobile edge computing (WP-MEC). The sustainable energy supply and adequate computing capabilities are provided for wireless devices in WP-MEC. In this paper, we propose an online energy consumption minimization (OECM) algorithm for multiple IoT devices with latency constraints in WP-MEC environment. The nonorthogonal multiple access (NOMA) technique is used to address the preamble collision where two or more devices may select the same preamble. Based on Lyapunov optimization framework, optimal results of the time allocation for energy transfer and data transmission, the proportions of offloading tasks, data transmit powers and CPU-cycle frequencies are achieved in each time block by an iterative algorithm which leverages a convex approximation approach. The theoretical performance analysis shows that there is an upper bound of the system energy consumption. Simulation results verify that our proposed OECM algorithm outperforms the scheme without wireless energy transfer and other benchmark algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing Wireless energy transfer NOMA IoT

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Theoretical study of cellulose II nanocrystals with different exposed facets

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SCIENTIFIC REPORTS 2021年第1期11卷 1-11页

作者： Leng, Can Li, Kenli Tian, Zean Si, Yubing Huang, Huang Li, Junfeng Liu, Jie Huang, Wei-Qing Li, Keqin Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China Natl Supercomp Ctr Changsha Changsha 410082 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Zhengzhou Univ Coll Chem Zhengzhou 450001 Peoples R China Luoyang Normal Univ Coll Chem & Chem Engn Luoyang 471934 Peoples R China Luoyang Normal Univ Henan Key Lab Funct Oriented Porous Mat Luoyang 471934 Peoples R China Hunan Univ Sch Phys & Elect Dept Appl Phys Changsha 410082 Peoples R China State Univ New York New Paltz Dept Comp Sci New Paltz NY 12561 USA

Derived from the most abundant natural polymer, cellulose nanocrystal materials have attracted attention in recent decades due to their chemical and mechanical properties. However, still unclear is the influence of different exposed facets of the cellulose nanocrystals on the physicochemical properties. Herein, we first designed cellulose II nanocrystals with different exposed facets, the hydroxymethyl conformations distribution, hydrogen bond (HB) analysis, as well as the relative structural stability of these models (including crystal facets {A, B, O} and Type-A models vary in size) are theoretically investigated. The results reveal that the HB network of terminal anhydroglucose depends on the adjacent chain's contact sites in nanocrystals exposed with different facets. Compared to nanocrystals exposed with inclined facet, these exposed with flat facet tend to be the most stable. Therefore, the strategy of tuning exposed crystal facets will guide the design of novel cellulose nanocrystals with various physicochemical properties.

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Sensor Fusion-Based Cooperative Trail Following for Autonomous Multi-Robot System

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SENSORS 2019年第4期19卷 823-823页

作者： Geng, Mingyang Liu, Shuqi Wu, Zhaoxia Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China Northeastern Univ Natl Key Lab Big Data Management & Anal Shenyang 110000 Liaoning Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Peoples R China

Autonomously following a man-made trail in the wild is a challenging problem for robotic systems. Recently, deep learning-based approaches have cast the trail following problem as an image classification task and have achieved great success in the vision-based trail-following problem. However, the existing research only focuses on the trail-following task with a single-robot system. In contrast, many robotic tasks in reality, such as search and rescue, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to lead to a more robust performance and perform the trail-following task in a better manner. Concretely, each robot can periodically exchange the vision data with other robots and make decisions based both on its local view and the information from others. This paper proposes a sensor fusion-based cooperative trail-following method, which enables a group of robots to implement the trail-following task by fusing the sensor data of each robot. Our method allows each robot to face the same direction from different altitudes to fuse the vision data feature on the collective level and then take action respectively. Besides, considering the quality of service requirement of the robotic software, our method limits the condition to implementing the sensor data fusion process by using the threshold mechanism. Qualitative and quantitative experiments on the real-world dataset have shown that our method can significantly promote the recognition accuracy and lead to a more robust performance compared with the single-robot system.

关键词： trail following cooperative perception multi-robot system feature fusion

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NTIRE 2021 Challenge on Perceptual Image Quality Assessment

NTIRE 2021 Challenge on Perceptual Image Quality Assessment

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Gu, Jinjin Cai, Haoming Dong, Chao Ren, Jimmy S. Qiao, Yu Gu, Shuhang Timofte, Radu Cheon, Manri Yoon, Sungjun Kang, Byungyeon Kangg Lee, Junwoo Zhang, Qing Guo, Haiyang Bin, Yi Hou, Yuqing Luo, Hengliang Guo, Jingyu Wang, Zirui Wang, Hai Yang, Wenming Bai, Qingyan Shi, Shuwei Xia, Weihao Cao, Mingdeng Wang, Jiahao Chen, Yifan Yang, Yujiu Li, Yang Zhang, Tao Feng, Longtao Liao, Yiting Li, Junlin Thong, William Pereira, Jose Costa Leonardis, Ales McDonagh, Steven Xu, Kele Yang, Lehan Cai, Hengxing Sun, Pengfei Ayyoubzadeh, Seyed Mehdi Royat, Ali Fezza, Sid Ahmed Hammou, Dounia Hamidouche, Wassim Ahn, Sewoong Yoon, Gwangjin Tsubota, Koki Akutsu, Hiroaki Aizawa, Kiyoharu Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China SenseTime Res Hong Kong Peoples R China OPPO Res Zurich Switzerland Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland LG Elect Seoul South Korea Meituan Grp Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Dept Elect Engn Beijing Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Peking Univ Beijing Peoples R China Bytedance Inc Beijing Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China Key Lab Parallel & Distributed Proc Sydney NSW Australia Univ Sydney Sydney NSW Australia 4Paradigm Inc Beijing Peoples R China Univ Zurich Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland McMaster Univ ECE Dept Hamilton ON Canada Sharif Univ Technol EE Dept Tehran Iran Natl Inst Telecommun & ICT Oran Algeria Univ Rennes INSA Rennes CNRS IETRUMR 6164 Rennes France SI Analyt Mainz Germany Univ Tokyo Tokyo Japan Hitachi Ltd Tokyo Japan

ISBN: (纸本)9781665448994

This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a new type of image processing technology, perceptual image processing algorithms based on Generative Adversarial Networks (GAN) have produced images with more realistic textures. These output images have completely different characteristics from traditional distortions, thus pose a new challenge for IQA methods to evaluate their visual quality. In comparison with previous IQA challenges, the training and testing datasets in this challenge include the outputs of perceptual image processing algorithms and the corresponding subjective scores. Thus they can be used to develop and evaluate IQA methods on GAN-based distortions. The challenge has 270 registered participants in total. In the final testing stage, 13 participating teams submitted their models and fact sheets. Almost all of them have achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.

关键词： Image quality Training Visualization Conferences Generative adversarial networks Distortion Market research

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