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共 4639 条记录，以下是961-970 订阅

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Controlling Garbage Collection and Request Admission to Improve performance of FaaS Applications

Controlling Garbage Collection and Request Admission to Impr...

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International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： David Quaresma Daniel Fireman thiago Emmanuel Pereira Federal University of Campina Grande Campina Grande Brazil Federal Institute of Alagoas Arapiraca Brazil

ISBN: (数字)9781728199245

ISBN: (纸本)9781728199252

Runtime environments like Java's JRE, .NET's CLR, and Ruby's MRI, are popular choices for cloud-based applications and particularly in the Function as a Service (FaaS) serverless computing context. A critical component of runtime environments of these languages is the garbage collector (GC). the GC frees developers from manual memory management, which could potentially ease development and avoid bugs. the benefits of using the GC come with a negative impact on performance; that impact happens because either the GC needs to pause the runtime execution or competes with the running program for computational resources. In this work, we evaluated the usage of a technique - Garbage Collector Control Interceptor (GCI) - that eliminates the negative impact of GC on performance by controlling GC executions and transparently shedding requests while the collections are happening. We executed experiments simulating AWS Lambda's behavior and found that GCI is a viable solution. It benefited the user by improving the response time up to 10.86% at 99.9th percentile and reducing cost by 7.22%, but it also helped the platform provider by improving resource utilization by 14.52%.

关键词： FAA Time factors Memory management Cloud computing Runtime environment Load modeling Java

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Context-Aware Collaborative Ranking 15th

Context-Aware Collaborative Ranking

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15th Asia Information Retrieval Societies Conference, AIRS 2019

作者： Dai, Wei Pan, Weike Ming, Zhong National Engineering Laboratory for Big Data System Computing Technology College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783030428341

Recommender systems (RS) are being used in a broad range of applications, from online shopping websites to music streaming platforms, which aim to provide users high-quality personalized services. Collaborative filtering (CF) is a promising technique to ensure the accuracy of a recommender system, which can be divided into specific tasks such as rating prediction and item ranking. However, there is a larger volume of published works studying the problem of rating prediction, rather than item ranking though it is recognized to be more appropriate for the final recommendation in a real application. On the other hand, many studies on item ranking devoted to leveraging implicit feedback are limited in performance improvements due to the uniformity of implicit feedback. Hence, in this paper, we focus on item ranking with informative explicit feedback, which is also called collaborative ranking. In particular, we propose a novel recommendation model termed context-aware collaborative ranking (CCR), which adopts a logistic loss function to measure the predicted error of ranking and exploits the inherent preference context derived from the explicit feedback. Moreover, we design an elegant strategy to distinguish between positive and negative samples used in the process of model training. Empirical studies on four real-world datasets clearly demonstrate that our CCR outperforms the state-of-the-art methods in terms of various ranking-oriented evaluation metrics. © Springer Nature Switzerland AG 2020.

关键词： Collaborative filtering

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Cambricon-F: Machine Learning computers with Fractal von Neumann architecture 19

Cambricon-F: Machine Learning Computers with Fractal von Neu...

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46th International symposium on computer architecture (ISCA) / Workshop on computer architecture Education (WCAE)

作者： Zhao, Yongwei Du, Zidong Guo, Qi Liu, Shaoli Li, Ling Xu, Zhiwei Chen, Tianshi Chen, Yunji Chinese Acad Sci Inst Comp Technol SKL Comp Architecture Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Cambricon Tech Ltd Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (纸本)9781450366694

Machine learning techniques are pervasive tools for emerging commercial applications and many dedicated machine learning computers on different scales have been deployed in embedded devices, servers, and data centers. Currently, most machine learning computer architectures still focus on optimizing performance and energy efficiency instead of programming productivity. However, with the fast development in silicon technology, programming productivity, including programming itself and software stack development, becomes the vital reason instead of performance and power efficiency that hinders the application of machine learning computers. In this paper, we propose Cambricon-F, which is a series of homogeneous, sequential, multi-layer, layer-similar, machine learning computers with the same ISA. A Cambricon-F machine has a fractal von Neumann architecture to iteratively manage its components: it is with von Neumann architecture and its processing components (sub-nodes) are still Cambricon-F machines with von Neumann architecture and the same ISA. Since different Cambricon-F instances with different scales can share the same software stack on their common ISA, Cambricon-Fs can significantly improve the programming productivity. Moreover, we address four major challenges in Cambricon-F architecture design, which allow Cambricon-F to achieve a high efficiency. We implement two Cambricon-F instances at different scales, i.e., Cambricon-F100 and Cambricon-F1. Compared to GPU based machines (DGX-1 and 1080Ti), Cambricon-F instances achieve 2.82x, 5.14x better performance, 8.37x, 11.39x better efficiency on average, with 74.5%, 93.8% smaller area costs, respectively.

关键词： computer architecture fractals learning (artificial intelligence) power aware computing power aware computing computer architecture Fractals Artificial Intelligence Machine Learning computers Programming different scale Building construction architecture Si technology Embedded device

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Ifme: Influence function based model explanation for black box decision systems 10th

Ifme: Influence function based model explanation for black b...

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10th International symposium on Parallel architectures, Algorithms and Programming, PAAP 2019

作者： Zha, Benbo Shen, Hong School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Computer Science University of Adelaide Adelaide Australia

Due to the high precision and the huge prediction needs, machine learning models based decision systems has been widely adopted in all works of life. they were usually constructed as a black box based on sophisticated, opaque learning models. the lack of human understandable explanation to the inner logic of these models and the reason behind the predictions from such systems causes a serious trust issue. Interpretable Machine Learning methods can be used to relieve this problem by providing an explanation for the models or predictions. In this work, we focus on the model explanation problem, which study how to explain the black box prediction model globally through human understandable explanation. We propose the Influence Function based Model Explanation (IFME) method to provide interpretable model explanation based on key training points selected through influence function. First, our method introduces a novel local prediction interpreter, which also utilizes the key training points for local prediction. then it finds the key training points to the learning models via influence function globally. Finally, we provide the influence function based model agnostic explanation to the model used. We also show the efficiency of our method through both theoretical analysis and simulated experiments. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Machine learning

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Software-Defined FPGA Accelerator Design for Mobile Deep Learning Applications 15th

Software-Defined FPGA Accelerator Design for Mobile Deep Lea...

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15th International symposium on Applied Reconfigurable computing

作者： Mousouliotis, Panagiotis G. Petrou, Loukas P. Aristotle Univ Thessaloniki Fac Engn Dept Elect & Comp Engn Div Elect & Comp Engn Thessaloniki 54124 Greece

ISBN: (纸本)9783030172268;9783030172275

Convolutional neural networks (CNNs) have been successfully used to attack problems such as object recognition, object detection, semantic segmentation, and scene understanding. the rapid development of deep learning goes hand by hand with the adaptation of GPUs for accelerating its processes, such as network training and inference. Even though FPGA design exists long before the use of GPUs for accelerating computations and despite the fact that high-level synthesis (HLS) tools are getting more attractive, the adaptation of FPGAs for deep learning research and application development is poor due to the requirement of hardware design related expertise. this work presents a workflow for deep learning mobile application acceleration on small low-cost low-power FPGA devices using HLS tools. this workflow eases the design of an improved version of the SqueezeJet accelerator used for the speedup of mobile-friendly low-parameter ImageNet class CNNs, such as the SqueezeNet v1.1 and the ZynqNet. Additionally, the workflow includes the development of an HLS-driven analytical model which is used for performance estimation of the accelerator.

关键词： FPGA accelerator high-level synthesis Mobile embedded systems CNN Deep learning application

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PIM-VR: Erasing Motion Anomalies In highly-Interactive Virtual Reality World With Customized Memory Cube 25

PIM-VR: Erasing Motion Anomalies In Highly-Interactive Virtu...

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25th IEEE International symposium on high performance computer architecture (HPCA)

作者： Xie, Chenhao Zhang, Xingyao Li, Ang Fu, Xin Song, Shuaiwen Leon Univ Houston ECE Dept ECMOS Lab Houston TX 77204 USA Pacific Northwest Natl Lab HPC Grp Richland WA 99352 USA

ISBN: (纸本)9781728114446

With the revolutionary innovations emerging in the computer graphics domain, virtual reality (VR) has become increasingly popular and shown great potential for entertainment, medical simulation and education. In the highly interactive VR world, the motion-to-photon delay (MPD) which represents the delay from users' head motion to the responded image displayed on their head devices, is the most critical factor for a successful VR experience. Long MPD may cause users to experience significant motion anomalies: judder, lagging and sickness. In order to achieve the short MPD and alleviate the motion anomalies, asynchronous time warp (ATW) which is known as an image re-projection technique, has been proposed by VR vendors to map the previously rendered frame to the correct position using the latest head-motion information. However, after a careful investigation on the efficiency of the current GPU-accelerated ATW through executing real VR applications on modern VR hardware, we observe that the state-of-the-art ATW technique cannot deliver the ideal MPD and often misses the refresh deadline, resulting in reduced frame rate and motion anomalies. this is caused by two major challenges: inefficient VR execution model and intensive off-chip memory accesses. To tackle these, we propose a preemption-free Processing-In-Memory based ATW design which asynchronously executes ATW within a 3D-stacked memory, without interrupting the rendering tasks on the host GPU. We also identify a redundancy reduction mechanism to further simplify and accelerate the ATW operation. A comprehensive evaluation of our proposed design demonstrates that our PIM-based ATW can achieve the ideal MPD and provide superior user experience. Finally, we provide a design space exploration to showcase different design choices for the PIM-based ATW design, and the results show that our design scales well in future VR scenarios with higher frame resolution and even lower ideal MPD.

关键词： Rendering (computer graphics) Graphics processing units Resists Head Hardware Photonics Task analysis

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performance Comparison of Terraform and Cloudify as Multicloud Orchestrators

Performance Comparison of Terraform and Cloudify as Multiclo...

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IEEE/ACM International symposium on Cluster computing and the Grid (CCGRID)

作者： Leonardo Rebouças de Carvalho Aleteia Patricia Favacho de Araujo Dept. of Computer Science University of Brasília Brasília Brazil

ISBN: (数字)9781728160955

Studies indicate that by 2022 multicloud models will reach 75% of the cloud computing market. To handle the high-featured set of computing capability on this paradigm, often called "Sky computing", infrastructure tools such as cloud orchestrators has emerged. this paper analyzes the most referenced tools in the literature such as Cloudify, Heat, CloudFormation, Terraform and Cloud Assembly, as well as the TOSCA standard. the literature review, complemented by a practical experiment, revealed that Terraform and Cloudify presents great affinity with Sky computing scenarios. In the experiment Terraform outperformed Cloudify in several aspects.

关键词： Cloud computing Heating systems Tools Organizations Object oriented modeling Computational modeling computer architecture

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A 4GHz 16nm SRAM architecture with Low-Power Features for Heterogeneous computing Platforms 33

A 4GHz 16nm SRAM Architecture with Low-Power Features for He...

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39th symposium on VLSI Technology / 33rd symposium on VLSI Circuits

作者： Cakir, Cagla Chen, Andy W. Chong, Y. K. thyagarajan, Sriram McCartney, Mark Tan, Peixuan Shi, Yulin Bhargava, Mudit Arm Inc Austin TX 78735 USA

ISBN: (纸本)9784863487185

We present a high-performance 6T SRAM architecture equipped with low-power features of late cancel, left-right enable, input-gating, and power-gating. Measurements show that these SRAMs can support CPUs running at 46Hz while offering dynamic power savings of 17% and 6% for the caches and the system respectively and up to 21X static power system savings for the low-power implementation.

关键词： Random access memory Power measurement Power system dynamics Decoding Clocks Semiconductor device measurement computer architecture

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performance Evaluation of the Virtualization Environment of a Microservices-Based Payroll System

Performance Evaluation of the Virtualization Environment of ...

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Iberian Conference on Information Systems and Technologies (CISTI)

作者： Klayton Castro Lucas e M. C. Martins Polyane Wercelens Rafael Padilha Italo Gervasio Flávio E. G. de Deus William F. Giozza Rafael T. de Sousa Electrical Engineering Department University of Bras´ılia (UnB) Brasilia DF Brazil

ISBN: (数字)9789895465903

ISBN: (纸本)9781728167244

For better usage of idle resources in a symmetric multiprocessing environment, cloud computing providers often exploit the boundaries of parallelism by imposing high CPU subscription rates over their virtualization systems. Moreover, unsuitable resource allocation can significantly impair the performance during intensive workloads and increase infrastructure expenditures unnecessarily. It becomes an increasing challenge when dealing with microservices architecture and container encapsulated applications, for these are approaches that add more intricacy layers to the workload scheduling over the hardware. this paper presents a case study on a public agency private cloud, which aims to evaluate the performance of hypervisors and virtual machines, that are serving a Kubernetes container orchestration cluster and running a microservices-based Payroll System during CPU-bound behavior tasks. We also propose a model for sizing the virtual environment according to the best choices for the hardware characteristics and the workload needs.

关键词： Virtualization Containers Cloud computing computer architecture Hardware Virtual machine monitors Servers

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Second-Price Auction Based Cognitive Traffic Offloading in Heterogeneous Networks 15

Second-Price Auction Based Cognitive Traffic Offloading in H...

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15th IEEE International Wireless Communications and Mobile computing Conference (IEEE IWCMC)

作者： Du, Jun Jiang, Chunxiao Zhang, Haijun Ren, Yong Leung, Victor C. M. Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Space Ctr Beijing 100084 Peoples R China Univ Sci & Technol Beijing Dept Commun Engn Beijing 100083 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

ISBN: (纸本)9781538677476

Recently, increasingly heterogeneous wireless networks are expected to support extremely high data rates and exponentially increasing demands of data, which require new spectrum sharing technology paradigms. By achieving an efficient spectrum sharing among heterogeneous networks (HetNets), traffic offloading is a promising solution for boosting the capacity of traditional macro-cell networks. In this paper, a cognitive spectrum sharing and traffic offloading mechanism is proposed to realize the cooperation and competition between the macro-cell base station (MBS) and small-cell base stations (SBSs). Under the cooperation mode, the MBS stops occupying a corresponding channel, and a selected SBS helps offload the traffic from the MBS by exclusively using this channel. To facilitate the offloading negotiation between the MBS and SBSs, we design a second-price auction mechanism, which presents positive allocative externalities, i.e., other uncooperative SBSs can benefit from the cooperation between the MBS and the SBS performing offloading. Meanwhile, the unique optimal biding strategies for different SBSs to achieve the symmetric Bayesian equilibrium are derived and obtained in this paper. the performance of the proposed cognitive traffic offloading mechanism is validated in the simulations, which also reveal that there exists the unique optimal offloading threshold for the MBS to achieve the maximum utility.

关键词： Base stations Interchannel interference Heterogeneous networks computer architecture Radio spectrum management Probability distribution"

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