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CantorSim: Simplifying Acceleration of Micro-architecture Simulations

CantorSim: Simplifying Acceleration of Micro-architecture Si...

引用

International Symposium on Modeling, Analysis and Simulation of computer and Telecommunication Systems (MASCOTS)

作者： Zhibin Yu Hai Jin Jian Chen Lizy K. John Service Computing Technology and System Laboratory Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA

Many techniques have been developed to accelerate micro-architecture simulation since it is becoming increasingly urgent as the complexity of workloads and simulated processors increases. However, most of popular techniques need profiles or trial simulations to determine parameters before real simulations. When the number of dynamic instructions of workloads such as SPEC CPU2006 is huge, the profiles or trial simulations often take a long time. What's worse, any changes of benchmarks need to repeat the above work. This paper proposes a novel approach based on fractals to solve this problem. It employs the generation procedure of trisection Cantor Set, a classic fractal, to select instructions simulated in detail. We implement a simulator named CantorSim using the trisection Cantor fractal approach. The simulator is evaluated with benchmark programs from SPEC CPU2000 and SPEC CPU2006. We show that our approach is micro-architecture independent as the same parameter value for a single program yields similar results on processors with different configurations. The unified analytical model used to determine sampling parameters for different benchmarks indicates the proposed approach can save a lot of time for preparing parameters. On the other hand, for SPEC CPU2000 and SPEC CPU2006, CantorSim is slightly faster than SMARTSim. The validated average CPI relative errors of the SPEC CPU2000 and SPEC CPU2006 are 3.2% and 2.2% respectively. Therefore, our proposed approach can simplify acceleration of simulations significantly without sacrificing speed and accuracy.

关键词： Fractals Benchmark testing Acceleration Computational modeling Accuracy Predictive models Program processors

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Communication Cost Efficient Virtualized Network Function Placement for Big Data Processing

Communication Cost Efficient Virtualized Network Function Pl...

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IEEE Conference on computer Communications Workshops

作者： Lin Gu Sheng Tao Deze Zeng Hai Jin Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science China University of Geosciences Wuhan Hubei China

ISBN: (纸本)9781467399562

The data explosion in the emerging big data era imposes a big burden on the network infrastructure. This vision has urged the evolution of computer networks. By softwarizing traditional dedicated hardware based functions to virtualized network function (VNF) that can run on standard commodity servers, network function virtualization (NFV) technology promises increased networking efficiency, flexibility and scalability. From the perspective of network service providers, with the consideration of big data traffic volume, one primary concern is on the communication cost, which is highly influenced by the VNF placement strategy. In this paper, we are motivated to investigate the issue on communication cost efficient VNF placement problem for big data processing, with joint consideration of network flow balancing and the predetermined network service semantics. We formulate this problem into a mixed-integer linear programming (MILP) form and then propose a low-complexity relaxation-based heuristic algorithm accordingly. The high efficiency of our proposal is validated by extensive simulation studies.

关键词： Noise measurement Big data Distributed databases Algorithm design and analysis Servers Heuristic algorithms Semantics

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Jobgrid: Chinese College Graduates Employment Information grid System

JobGrid: Chinese College Graduates Employment Information Gr...

引用

Chinagrid Annual Conference (Chinagrid)

作者： Feng Zhao Bin Zhou Xiaoxing Sun Hai Jin Song Wu School of Computer Science and Technology Huazhong University of Science & Technology Wuhan China Cluster and Grid Computing Lab Wuhan China The State Key Laboratory of Virtual Reality Technology and Systems Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9780769533063

This paper proposes a realistic data grid system named Jobgrid to construct information platform for Chinese college graduates employment, which supports centralized transaction processing to ensure consistent, effective operation in data access, control, monitor and analysis, and distributed data management to provide heterogeneous data integration, conversion, exaction and export. Jobgrid has implemented global uniform view and massively heterogeneous data access, and provided efficiently data and transactions processing and cooperative control mechanism for distributed resource over grid.

关键词： Databases Educational institutions Distributed databases Servers Monitoring computer architecture Employment

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BookChain: Library-Free Book Sharing Based on Blockchain technology

BookChain: Library-Free Book Sharing Based on Blockchain Tec...

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

作者： Jiajie Zeng Xiaohai Dai Jiang Xiao Wenhui Yang Weifeng Hao 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

Modern bookcrossing leverages the mobile networks to help readers share books via convenient connection, and thus expedites the dissemination of information. However, the lack of traceability has significantly hindered the wide adoption of mobile bookcrossing, leading to loss of books. Meanwhile, the managerial inefficiency of current mobile bookcrossing systems becomes another obstacle for advancing the book circulation. To overcome these problems, we propose to leverage the promising blockchain technology with the merits of its immutability and cryptographic smart contract. In this paper, we present BookChain, a traceable and efficient blockchain-based innercampus book sharing system. BookChain stores the complete sharing data of an interested book permanently on blockchain, such that every reader can trace the borrowing history, which reduces the potential for loss of book. BookChain also introduces the use of smart contract to automate the circulation of books with minimal human intervention, resulting in the improvement of efficiency. The experimental results show the effectiveness and low-cost of the proposed system under high concurrent users.

关键词： Contracts Web servers Web pages

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Fast and Scalable Gate-Level Simulation in Massively Parallel Systems

Fast and Scalable Gate-Level Simulation in Massively Paralle...

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IEEE International Conference on computer-Aided Design

作者： Haichuan Hu Zichen Xu Yuhao Wang Fangming Liu Services Computing Technology and System Lab Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Mathematics and Computer Science Nanchang University Nanchang China Pengcheng Laboratory Shenzhen China

The natural bijection between a proposed circuit design and its graph representation shall allow any graph optimization algorithm deploying into many-core systems efficiently. However, this process suffers from the exponentially growing overhead and heavy memory footprint with the signal propagation. To conquer the unique challenge, we systematically study the simulation with millions of gates, and identify that the processing complexity could grow exponentially from the signal inputs, the skewness of the computational graph stays. Thus, we present ZhouBi, a fast and scalable gate-level simulation framework to fully exploit the parallelism from many-core systems. ZhouBi contributes in threefolds, (I) a graph representation that colors gate-level netlists and identifies skew partitions based on the graph skewness; (II) A set of heuristic algorithms that picks opportunistic and conservative algorithms to accelerate the simulation; (III) A system facility that supports selective mapping between simulation and many-core, providing a tradeoff between the risk of concurrent simulation fail and performance gain. We have prototyped ZhouBi and evaluated it with practical baselines. ZhouBi can achieve a 27.6× performance gain, as compared to the state-of-the-practice Veriwell without compromising any correctness. Our framework supports large graphs enabling scale-out gate-level simulations for chip design.

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SumPA: Efficient Pattern-Centric Graph Mining with Pattern Abstraction 21

SumPA: Efficient Pattern-Centric Graph Mining with Pattern A...

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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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Grace: An Efficient Parallel SPARQL Query System over Large-Scale RDF Data

Grace: An Efficient Parallel SPARQL Query System over Large-...

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International Conference on computer Supported Cooperative Work in Design

作者： Xiang Kang Yuying Zhao Pingpeng Yuan 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

As a markup language for describing web resources, RDF is often used to represent graph data. SPARQL is a standard query language for RDF data, which is convenient in querying RDF. As RDF data grows rapidly, how to deal with complex queries over large-scale data in a reasonable time still remains many challenges. The existing RDF query systems often fail to respond within a reasonable time when dealing with complex SPARQL queries. Therefore, we propose an efficient parallel SPARQL query system and novel in three aspects. First, the proposed design provides a dynamic selectivity estimation strategy and generates an optimal query plan for parallel query processing. Second, the new design proposes a parallel processing model to maximize the parallelism of the system. Finally, chunk-based task distribution strategy is implemented to assist the parallel processing model. Based on the proposed design, we implement an efficient parallel query system (Grace). Extensive experiments on LUBM and BTC benchmarks show that Grace outperforms RDF-3X, Virtuoso, TripleBit and achieves a good performance of scalability on the number of threads.

关键词： Distribution strategy Scalability Query processing Markup languages Instruction sets Estimation Parallel processing

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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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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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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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