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检索条件"任意字段=15th Symposium on Computer Architecture and High Performance Computing"

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BFPA: Butterfly Strategy Flower Pollination Algorithm 15th

BFPA: Butterfly Strategy Flower Pollination Algorithm

引用

15th International Conference on Intelligent computing, ICIC 2019

作者： Lei, Mengyi Luo, Qifang Zhou, Yongquan Tang, Chengmei Gao, Yuanyuan College of Information Science and Engineering Guangxi University for Nationalities Nanning530006 China Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence Nanning530006 China

ISBN: (纸本)9783030267629

Aiming at the shortcomings of slow convergence and low precision of flower pollination algorithm, a flower pollination algorithm based on butterfly pollination strategy (BFPA) was proposed. the algorithm first uses the butterfly pollination strategy to accelerate the convergence speed of the global search phase. Second, in the local search phase, the beetle antenna search help algorithm is used to jump out of the local optimum. the experiment uses five Benchmark test functions to test, and the results show that the BFPA algorithm has better performance than other versions of the pollination algorithm. © 2019, Springer Nature Switzerland AG.

关键词： Global optimization

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In-situ AI: Towards Autonomous and Incremental Deep Learning for IoT Systems 24

In-situ AI: Towards Autonomous and Incremental Deep Learning...

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24th IEEE International symposium on high performance computer architecture (HPCA)

作者： Song, Mingcong Zhong, Kan Zhang, Jiaqi Hu, Yang Liu, Duo Zhang, Weigong Wang, Jing Li, Tao Univ Florida Gainesville FL 32611 USA Chongqing Univ Chongqing Peoples R China Univ Texas Dallas Richardson TX 75083 USA Capital Normal Univ Beijing Peoples R China

ISBN: (纸本)9781538636596

Recent years have seen an exploration of data volumes from a myriad of IoT devices, such as various sensors and ubiquitous cameras. the deluge of IoT data creates enormous opportunities for us to explore the physical world, especially with the help of deep learning techniques. Traditionally, the Cloud is the option for deploying deep learning based applications. However, the challenges of Cloud-centric IoT systems are increasing due to significant data movement overhead, escalating energy needs, and privacy issues. Rather than constantly moving a tremendous amount of raw data to the Cloud, it would be beneficial to leverage the emerging powerful IoT devices to perform the inference task. Nevertheless, the statically trained model could not efficiently handle the dynamic data in the real in-situ environments, which leads to low accuracy. Moreover, the big raw IoT data challenges the traditional supervised training method in the Cloud. To tackle the above challenges, we propose In-situ AI, the first Autonomous and Incremental computing framework and architecture for deep learning based IoT applications. We equip deep learning based IoT system with autonomous IoT data diagnosis (minimize data movement), and incremental and unsupervised training method (tackle the big raw IoT data generated in ever-changing in-situ environments). To provide efficient architectural support for this new computing paradigm, we first characterize the two In-situ AI tasks (i.e. inference and diagnosis tasks) on two popular IoT devices (i.e. mobile GPU and FPGA) and explore the design space and tradeoffs. Based on the characterization results, we propose two working modes for the In-situ AI tasks, including Single-running and Co-running modes. Moreover, we craft analytical models for these two modes to guide the best configuration selection. We also develop a novel two-level weight shared In-situ AI architecture to efficiently deploy In-situ tasks to IoT node. Compared with traditional IoT sy

关键词： Task analysis Machine learning Cloud computing Data models Training computer architecture

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Radio Resource Allocation Scheme for Drone-Assisted AR Applications 15

Radio Resource Allocation Scheme for Drone-Assisted AR Appli...

引用

15th International symposium on Pervasive Systems, Algorithms and Networks (I-SPAN)

作者： Meng, Yang Meng, Qingmin Yue, Wenjing Zou, Yulong Wang, Xiaoming Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210003 Jiangsu Peoples R China

ISBN: (纸本)9781538685341

Mobile edge computing facilitates the implementation of computationally intensive applications on mobile devices, such as Mobile Augmented Reality (MAR). the MAR application usually includes three modules, namely, data collection in the uplink, edge computing and data transmission in the downlink, and the cooperation between the modules will help improve the energy consumption performance of the AR system. We propose an integrated MAR transmission design with companion drones and a high energy efficiency resource allocation scheme. through a successive convex approximation (SCA) algorithm, we convert non convex problems into convex ones for optimal solution. Compared with traditional independent offloading by users, joint relay transmission and shared uplink data offloading schemes can produce significant gains in energy efficiency.

关键词： Mobile edge computing Mobile augmented reality Shared offloading Companion drone SCA algorithm

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Integration of Multimodal Data for Breast Cancer Classification Using a Hybrid Deep Learning Method 15th

Integration of Multimodal Data for Breast Cancer Classificat...

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15th International Conference on Intelligent computing, ICIC 2019

作者： Yan, Rui Ren, Fei Rao, Xiaosong Shi, Baorong Xiang, Tiange Zhang, Lingling Liu, Yudong Liang, Jun Zheng, Chunhou Zhang, Fa College of Computer Science and Technology Anhui University Hefei China High Performance Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science Wuhan University Wuhan China School of Computer Science The University of Sydney Sydney Australia Department of Pathology Peking University International Hospital Beijing China

ISBN: (纸本)9783030267629

Although the application of deep learning has greatly improved the performance of benign and malignant breast cancer classification algorithm, the accuracy of classification using only the pathological image has been unable to meet the requirements of clinical practice. Inspired by the real scene when the pathologist read the pathological image for diagnosis, in this paper, we propose a new hybrid deep learning method for benign and malignant breast cancer classification. From the perspective of multimodal data fusion, our method combines pathological image and structured data in the clinical electronic medical record (EMR) to further improve the accuracy of breast cancer classification. thus, the proposed method can be useful for breast cancer diagnosis in real clinical practice. Experimental results based on our datasets show that the proposed method significantly outperforms the state-of-the-art methods in terms of overall classification accuracy. © 2019, Springer Nature Switzerland AG.

关键词： Image classification

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Samsara architecture: Exploring Situation Awareness in Cloud computing Management 19

Samsara Architecture: Exploring Situation Awareness in Cloud...

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19th symposium on high-performance computing Systems (WSCAD)

作者： Neves, Vilnei Pilla, Mauricio Yamin, Adenauer Pilla, Laercio Fed Univ Pelotas UFPel Postgrad Program Comp PPGC Pelotas Brazil Grenoble Alpes Univ LIG Grenoble INP InriaCNRS Grenoble France

ISBN: (纸本)9781728137728

Issues related to energy consumption and its efficiency in large-scale computing environments have emerged as critical points in the development of modern computer systems. this article presents the Samsara architecture concept, which aims to manage energy efficient computational clouds. the architecture was developed exploring situation awareness strategies, operating autonomously and with minimal human intervention, essential aspects in massive data processing centers. Samsara has been implemented as one of the modules of the OpenStack cloud platform and considers the maximum allocation capacity of each physical machine, to consolidate the workloads. In the evaluations carried out with synthetic loads, reductions of up to 12.3 % in the energy consumption in the managed computational infrastructure were achieved, demonstrating the potential of Samsara for the operation of computational clouds.

关键词： Cloud computing Energy Efficiency Situation Awareness

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Perception-Oriented 3D Rendering Approximation for Modern Graphics Processors 24

Perception-Oriented 3D Rendering Approximation for Modern Gr...

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24th IEEE International symposium on high performance computer architecture (HPCA)

作者： Xie, Chenhao Fu, Xin Song, Shuaiwen Leon Univ Houston ECMOS Lab ECE Dept Houston TX 77004 USA PNNL High Performance Comp Grp Richland WA USA

ISBN: (纸本)9781538636596

Anisotropic filtering enabled by modern rasterization-based GPUs provides users with extremely authentic visualization experience, but significantly limits the performance and energy efficiency of 3D rendering process due to its large texture data requirement. To improve 3D rendering efficiency, we build a bridge between anisotropic filtering process and human visual system by analyzing users' perception on image quality. We discover that anisotropic filtering does not impact user perceived image quality on every pixel. this motives us to approximate the anisotropic filtering process for non-perceivable pixels in order to improve the overall 3D rendering performance without damaging user experience. To achieve this goal, we propose a perception-oriented runtime approximation model for 3D rendering by leveraging the inner-relationship between anisotropic and isotropic filtering. We also provide a low-cost texture unit design for enabling this approximation. Extensive evaluation on modern 3D games demonstrates that, under a conservative tuning point, our design achieves a significant average speedup of 17% for the overall 3D rendering along with 11% total GPU energy reduction, without visible image quality loss from users' perception. It also reduces the texture filtering latency by an average of 29%. Additionally, it creates a unique perception-based tuning space for performance-quality tradeoffs on graphics processors.

关键词： GPU Approximate computing 3D Rendering User-Oriented Study

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Improved Spectral Clustering Method for Identifying Cell Types from Single-Cell Data 1

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15th International Conference on Intelligent computing, ICIC 2019

作者： Li, Yuanyuan Luo, Ping Lu, Yi Wu, Fang-Xiang Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada School of Mathematics and Physics Wuhan Institute of Technology Wuhan China Department of Mechanical Engineering University of Saskatchewan Saskatoon Canada Department of Computer Science University of Saskatchewan Saskatoon Canada

ISBN: (数字)9783030269692

ISBN: (纸本)9783030269685

With the development of single-cell RNA sequencing (scRNA-seq) technology, characterizing heterogeneity at the cellular level has become a new area of computational biology research. However, the infiltration of different types of cells and the high variability in gene expression complicate classification of cell types. In this study, we propose an improved spectral clustering method for clustering single-cell data that avoid the overfitting issue and consider both similarity and dissimilarity, motivated by the observation that same type cells have similar gene expression patterns, but different types of cells produce dissimilar gene expression patterns. To evaluate the performance of the proposed spectral clustering method, we compare it with the traditional spectral clustering method in recognizing cell types on various real scRNA-seq data. the results show that taking intercellular dissimilarity into account can effectively achieve high accuracy and robustness and that our method outperforms the traditional spectral clustering methods in grouping cells that belong to the same cell types. © 2019, Springer Nature Switzerland AG.

关键词： RNA

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Large-Scale and high-throughput QR Decomposition on an FPGA

Large-Scale and High-Throughput QR Decomposition on an FPGA

引用

Annual IEEE symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Dajung Lee Andrei Hagiescu Dan Pritsker Intel Corporation San Diego CA USA Intel Corporation Toronto Canada

the QR decomposition, also called the QR factorization, is one of core matrix operations that is used to solve a linear inverse/solver problem. We develop a high-throughput QR decomposition for large-scale matrices on FPGA. We refine the modified Gram-Schmit QRD algorithm into a hardware-friendly algorithmic flow and describe the core architecture in C. We synthesize our code using an Intel FPGA SDK for OpenCL targeting an Intel Arria 10 FPGA device.

关键词： Field programmable gate arrays Matrix decomposition performance evaluation throughput computer architecture Hardware Graphics processing units

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Spark-Based Distributed Quantum-Behaved Particle Swarm Optimization Algorithm 15th

Spark-Based Distributed Quantum-Behaved Particle Swarm Optim...

引用

15th International Conference on Cooperative Design, Visualization, and Engineering (CDVE)

作者： Zhang, Zhaojuan Wang, Wanliang Gao, Nan Zhao, Yanwei Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China

ISBN: (纸本)9783030005603;9783030005597

For high dimensional and complex tasks, quantum optimization algorithms suffer from the problem of high computational cost. Distributed computing is an efficient way to solve such problems. therefore, distributed optimization algorithms have become a hotspot for large scale optimization problems with the increasing volume of the data. In this paper, a novel Spark-based distributed quantum-behaved particle swarm optimization algorithm (SDQPSO) was proposed. By submitting the task to a higher computing cluster in parallel, the SDQPSO algorithm can improve the convergence performance.

关键词： Spark Distributed computing Quantum optimization Big data

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Analysis and Optimization of Pipelined Broadcast Algorithms on Gigabit Ethernet and InfiniBand Networks

Analysis and Optimization of Pipelined Broadcast Algorithms ...

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International Asian School-Seminar Optimization Problems of Complex Systems

作者： Mikhail Kurnosov Daniil Berlizov Tatiana Tkacheva Elizaveta Tokmasheva Siberian State University of Telecommunications and Information Sciences

ISBN: (纸本)9781728129877

theoretical and experimental analysis of MPI_Bcast algortihms is presented. the optimal tree degrees and segment sizes for pipelined versions of algorithms are obtained. Algorithms were investigated according to their implementation in the Open MPI library. theoretical results are consistent with experiments on a computer cluster with Gigabit Ethernet and InfiniBand communication networks.

关键词： Broadcast MPI Parallel programming high-performance computing Gigabit Ethernet high performance computing Parallel programming message passing mannose phosphate isomerase remote procedure calls Infiniband broadcasting algorithms Analysis

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