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Proceedings of the 30th International Conference on Parallel Architectures and Compilation Techniques

作者： Chuangyi Gui Xiaofei Liao Long Zheng Pengcheng Yao Qinggang Wang Hai Jin National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665442787

Graph mining aims to explore interesting structural information of a graph. Pattern-centric systems typically transform a generic-purpose graph mining problem into a series of subgraph matching problems for high performance. Existing pattern-centric mining systems reduce the substantial search space towards a single pattern by exploring a highly-optimized matching order, but inherent computational redundancies of such a matching order itself still suffer severely, leading to significant performance degradation. The key innovation of this work lies in a general redundancy criterion that characterizes computational redundancies arising in not only handing a single pattern but also matching multiple patterns simultaneously. In this paper, we present SumPA, a high-performance pattern-centric graph mining system that can sufficiently remove redundant computations for any complex graph mining problems. SumPA features three key designs: (1) a pattern abstraction technique that can simplify numerous complex patterns into a few simple abstract patterns based on pattern similarity, (2) abstraction-guided pattern matching that completely eliminates (totally and partially) redundant computations during subgraph enumeration, and (3) a suite of system optimizations to maximize storage and computation efficiency. Our evaluation on a wide variety of real-world graphs shows that SumPA outperforms the two state-of-the-art systems Peregrine and GraphPi by up to 61.89× and 8.94×, respectively. For many mining problems on large graphs, Peregrine takes hours or even days while SumPA finishes in only a few minutes.

关键词： data reuse

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Fine-grained Scheduling in FPGA-Based Convolutional Neural Networks

Fine-grained Scheduling in FPGA-Based Convolutional Neural N...

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IEEE International Conference on Cloud computing and big data Analysis (ICCCBDA)

作者： Wei Zhang Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160252

FPGA has been considered as a promising solution to accelerate Convolutional Neural Networks (CNNs) for its excellent performance in energy efficiency and programmability. However, prior designs are usually designed for inference only as designers can map pre-trained models to the hardware in a very efficient way. However, those approaches may not be suitable for training CNN models. In this paper, we propose FConv, in which the CPU and FPGA work together in a fine-grained manner. The FPGA accelerator in FConv uses one Winograd-based convolver, which reduces the design complexity and improves performance. We apply double-buffer for output routine to effectively overlap computation and data transfer. We also integrate multiple PEs to improve data parallelism. We propose our analytical model for prediction and use it as a guide in task scheduling. We find the upper limit of performance under the current design based on the analytical model. We evaluate our design on VGG-16 and Densnet-40 on ImageNet and CIFAR-10. We achieve 262.43 GOP/s on the VGG-16 model, which is 2.13× of the performance compared to FFT-based implementation on the same platform. We also achieve at most 4×+ performance improvement compared MKL with 20 threads running on 10 core Intel processors.

关键词： Field programmable gate arrays Computational modeling Training Convolution Acceleration Bandwidth Task analysis

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FLoc: Fingerprint-Based Indoor Localization system under a Federated Learning Updating Framework

FLoc: Fingerprint-Based Indoor Localization System under a F...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yuxiang Liu Huichuwu Li Jiang Xiao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728152127

ISBN: (纸本)9781728152134

Fingerprinting-based indoor localization via WiFi has achieved a great breakthrough in the past decade. However, it suffers from an inherent problem that the localization accuracy declines sharply over time due to the dynamic environment and unstable WiFi devices. Researchers have designed many methods to update the localization model, e.g., crowdsourcing-based model updating, in order to maintain the localization accuracy. Unfortunately, they have not taken the privacy into consideration during the updating process. This will lead to a threat that the eavesdroppers could guess the location providers' private information according to the updating model. For the goal of maintaining the localization accuracy without the risk of privacy breaching, we proposed FLoc, a fingerprinting-based indoor localization system which updates the localization model via a federated learning framework. In FLoc, every provider maintains a local localization model in their own device. They will regularly encrypt the updating parameters and share them to a common model server. At the model server, it aggregates the encrypted information of the local models to maintain a general model, which will be sent back to the local devices for next updating iteration. We evaluate FLoc in an APs unknown laboratory corridor. The experiment results show that FLoc has a comparable localization performance. Moreover, it can successfully protect the providers' privacy, since the information transferred is all encrypted.

关键词： Mobile handsets Privacy Fingerprint recognition Servers Wireless fidelity databases Computational modeling

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Zihan Wang Siyang Song Cheng Luo Yuzhi Zhou Shiling Wu Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Computing and Mathematical Sciences University of Leicester

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5.

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Reasoning Based Workload Performance Prediction in Cloud data Centers

Reasoning Based Workload Performance Prediction in Cloud Dat...

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IEEE International Conference on Cloud computing technology and Science

作者： Adeel Aslam Hanhua Chen Jiang Xiao Hai Jin National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Cloud computing provides utility-based and scalable services to end-users. In the past decade, the demands for resource management in cloud computing have increased substantially which lead to certain challenges such as optimal resource utilization, power consumption, and service level agreement violations. Workload performance prediction serves as an assistance to address these issues. In this paper, we propose a prediction model based on clustered Case-Based Reasoning (CBR). The proposed model determines the performance metrics for workload prior to the co-operation of autonomic computing characteristics. Thus, CBR provides optimal scheduling of resources and workload monitoring for cloud data centers. In order to validate the proposed CBR-based prediction model, we perform a series of experiments and evaluate the effectiveness in terms of precision, recall, f-measure, and mean square error rate. We generate the cases for CBR using traces from the Google cluster data center. Moreover, we also validate our proposed prediction model against Support Vector Machine (SVM) prediction scheme. Experimental results show that the proposed CBR outperforms the SVM-based approach and yields 10% improvement in terms of precision.

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HiNUMA: NUMA-Aware data Placement and Migration in Hybrid Memory systems

HiNUMA: NUMA-Aware Data Placement and Migration in Hybrid Me...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Zhuohui Duan Haikun Liu Xiaofei Liao Hai Jin Wenbin Jiang Yu Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Non-uniform memory access (NUMA) architectures feature asymmetrical memory access latencies on different CPU nodes. Hybrid memory systems composed of non-volatile memory (NVM) and DRAM further diversify memory access latencies due to the relatively large performance gap between NVM and DRAM. Traditional NUMA memory management policies fail to manage hybrid memories effectively and may even hurt application performance. In this paper, we present HiNUMA, a new NUMA abstraction for memory allocation and migration in hybrid memory systems. HiNUMA advocates NUMA topologyaware hybrid memory allocation policies for the initial data placement. HiNUMA also proposes a new NUMA balancing mechanism called HANB for memory migration at runtime. HANB considers both data access frequency and memory bandwidth utilization to reduce the cost of memory accesses in hybrid memory systems. We evaluate the performance of HiNUMA with several typical workloads. Experimental results show that HiNUMA can effectively utilize hybrid memories, and deliver much higher application performance than conventional NUMA memory management policies and other state-of-the-art work.

关键词： Nonvolatile memory Random access memory Bandwidth Memory management Resource management Task analysis Memory architecture

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CPSAA: Accelerating Sparse Attention using Crossbar-based Processing-In-Memory Architecture

arXiv

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arXiv 2022年

作者： Li, Huize Jin, Hai Zheng, Long Liao, Xiaofei Huang, Yu Liu, Cong Xu, Jiahong Duan, Zhuohui Chen, Dan Gui, Chuangyi National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

The attention-based neural network attracts great interest due to its excellent accuracy enhancement. However, the attention mechanism requires huge computational efforts to process unnecessary calculations, significantly limiting the system’s performance. To reduce the unnecessary calculations, researchers propose sparse attention to convert some dense-dense matrices multiplication (DDMM) operations to sampled dense-dense matrix multiplication (SDDMM) and sparse matrix multiplication (SpMM) operations. However, current sparse attention solutions introduce massive off-chip random memory access since the sparse attention matrix is generally unstructured. We propose CPSAA, a novel crossbar-based processing-in-memory (PIM)-featured sparse attention accelerator to eliminate off-chip data transmissions. First, we present a novel attention calculation mode to balance the crossbar writing and crossbar processing latency. Second, we design a novel PIM-based sparsity pruning architecture to eliminate the pruning phase’s off-chip data transfers. Finally, we present novel crossbar-based SDDMM and SpMM methods to process unstructured sparse attention matrices by coupling two types of crossbar arrays. Experimental results show that CPSAA has an average of 89.6×, 32.2×, 17.8×, 3.39×, and 3.84× performance improvement and 755.6×, 55.3×, 21.3×, 5.7×, and 4.9× energy-saving when compare with GPU, FPGA, SANGER, ReBERT, and ReTransformer. Copyright © 2022, The Authors. All rights reserved.

关键词： Memory architecture

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Design and Simulation of Multi-tiered Heterogeneous Memory Architecture

Design and Simulation of Multi-tiered Heterogeneous Memory A...

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International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication systems (MASCOTS)

作者： Jinyuan Hu Haikun Liu Hai Jin Xiaofei Liao National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Heterogeneous memory systems have become increasingly popular in recent years. Because heterogeneous storage media often show significantly different characteristics in terms of bandwidth, latency, capacity, and energy consumption, it is still challenging to best utilize them for cost-efficient and energy-efficient heterogeneous memory systems. In this paper, we propose a simulation framework for multi-tiered heterogeneous memory architectures based on GEM5 and DRAMsim3 simulators. We design a heterogeneous memory controller to architect Non-Volatile Memory (NVM) as main memory, and architect both Dynamic Random Access Memory (DRAM) and High-Bandwidth Memory (HBM) as a hybrid cache of NVM. Specifically, HBM, DRAM, and NVM are managed in a single (flat) address space. However, we use an address remapping table to maintain the mappings between NVM pages and HBM/DRAM pages, and logically manage HBM/DRAM/NVM as a three-tiered hybrid memory system. We also design a hardware-supported hot page monitor based on Majority Element Algorithm (MEA) to identify the hottest pages in the DRAM, and a dynamic threshold adjustment scheme for hot page migration to balance the memory bandwidth between DRAM and HBM. Our multi-tiered heterogeneous memory architecture can take advantage of the large capacity of NVM, the low latency of DRAM, and the high bandwidth of HBM concurrently. Experimental results show that our tiered memory architecture can improve application performance by an average of $2.5\times$ compared with an NVM-only architecture, and up to 57.4% compared with a DRAM-only architecture. Moreover, the performance gap between our HBM/DRAM/NVM architecture and a HBM-only architecture is less than 10%.

关键词： Analytical models Nonvolatile memory Heuristic algorithms Computational modeling Memory architecture Random access memory Computer architecture

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A Real-time Demonstration of Bidirectional Multiformat/Rate-adjustable Integrated Laser Communication/Ranging for Satellite Communication

A Real-time Demonstration of Bidirectional Multiformat/Rate-...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Wang, Yizhou Wang, Yuanxiang Luo, Zhen Zhong, Linsheng Zou, Yang Ao, Xueyuan Liu, Jiale Fan, Feng Li, Xiaoliang Dai, Xiaoxiao Yang, Qi Liu, Deming National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Beijing Research Institute of Telemetry Beijing100094 China Jinyinhu Laboratory Wuhan430040 China

A multi-format/rate-adjustable integrated laser communication/ranging system is proposed, achieving adjustable rates between 0.625-10Gbit/s. Real-time experiment shows that at 1e-4 BER the ranging error is only 0.29% ... 详细信息

ISBN: (纸本)9781957171395

关键词： Laser theory

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The roles of urban buildings and vegetation in adjusting seasonal and daily air temperature 4

The roles of urban buildings and vegetation in adjusting sea...

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4th ISPRS Geospatial Week 2019

作者： Lan, Y. Huang, Z. Guo, R. Zhan, Q. Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services Shenzhen University China Guangdong Key Laboratory of Urban Informatics Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Urban Design Wuhan University Wuhan China

Exploring the spatiotemporal patterns of the relationships between urban indicators and urban temperature is essential to improve the mitigation effectiveness when we intend to adjust built environment for moderating urban thermal environment. In this study, RS, GIS technology and statistical methods were involved to investigate the spatiotemporal patterns of the impacts of urban buildings and vegetation on Air Temperature (AT). Building Density (BD) and Normalized Difference Vegetation Index (NDVI) are the indicators for urban buildings and vegetation respectively. The objectives of this study are: 1) to determine an appropriate scale for examining the building-AT relationships and vegetation-AT relationships;2) to explore the seasonal and daily characteristics of these relationships;and 3) to compare the effects of urban buildings and vegetation. The results show that, for both summer and winter, a scale of 200–250 m is optimal for examining building-AT relationships, and 960–1020 m is the desirable scale for studying vegetation-AT relationships. Based on the optimal scales, we find that for both buildings and vegetation, they only significantly impact night-time temperature in both summer and winter. For seasonal comparison, the building-AT relationships and vegetation-AT relationships are relatively stronger in summer than in winter, which are indicated by R-square of the regression results. When comparing the effects of urban building and vegetation, we find that increasing vegetation is more effective than reduce buildings to achieve the same air temperature reduction. Our findings are conducive to generating space-time targeted Urban Heat Island (UHI) mitigation strategies. © Authors 2019.

关键词： Vegetation

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