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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

共 1770 条记录，以下是121-130 订阅

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Proceedings - 2022 21st international symposium on Parallel and Distributed computing, ISPDC 2022

Proceedings - 2022 21st International Symposium on Parallel ...

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21st international symposium on Parallel and Distributed computing, ISPDC 2022

ISBN: (纸本)9781665488020

The proceedings contain 19 papers. The topics discussed include: deep heuristic for broadcasting in arbitrary networks;online event selection for Mu3e using GPUs;estimating the impact of communication schemes for distributed graph processing;a scalable algorithm for homomorphic computing on multi-core clusters;APPFIS: an advanced parallel programming framework for iterative stencil based scientific applications in HPC environments;a hybrid clustering algorithm for high-performance edge computing devices;performance modeling of scalable resource allocations with the imperial PEPA compiler;and investigating TCP/MPTCP support for drop computing in user space network stacks.

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Proceedings - 2015 IEEE 21st Pacific Rim international symposium on Dependable computing, PRDC 2015

Proceedings - 2015 IEEE 21st Pacific Rim International Sympo...

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21st IEEE Pacific Rim international symposium on Dependable computing, PRDC 2015

ISBN: (纸本)9781467393768

The proceedings contain 39 papers. The topics discussed include: FIRED - fault injector for reconfigurable embedded devices;NIRVANA: a non-intrusive black-box monitoring framework for rack-level fault detection;cooperative exceptions for concurrent objects;scheduling independent tasks with voltage overscaling;a smart fuzzing method for detecting heap-based buffer overflow in executable codes;cyber-physical topology language: definition, operations, and application;experimental evaluation of stealthy attack detection in a robot;a transformational approach for the hardening of region-adherent algorithms;application-oriented sensor network architecture for dependable structural health monitoring;and assessing the performance of phantom routing on source location privacy in wireless sensor networks.

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AI-oriented Workload Allocation for Cloud-Edge computing 21

AI-oriented Workload Allocation for Cloud-Edge Computing

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21st IEEE/ACM international symposium on Cluster, Cloud and Internet computing (CCGrid)

作者： Hao, Tianshu Zhan, Jianfeng Hwang, Kai Gao, Wanling Wen, Xu Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China

ISBN: (纸本)9781728195865

Different placement or collaboration policies in handling datasets and workloads across cloud, edge, and user-end may substantially affect a cloud-edge computing environment's overall performance. However, the common practice is to optimize the performance only on the edge layer, while ignoring the rest of the system. This paper calls attention to optimize AI-oriented workloads' performance across all components in cloud-edge architectures holistically. Our goal is to optimize AI-workload allocation in cloud clusters, edge servers, and end devices, achieving the minimum response time in latency-sensitive applications. This paper presents new workload allocation methods for AI workloads in cloud-edge computing systems. We have proposed two efficient allocation algorithms to reduce the end-to-end response time of single-workload and multi-jobs scenarios, respectively. We apply six edge AI workloads from a comprehensive edge computing benchmark - Edge AIBench for experiments. Besides, we conduct experiments in a real edge computing environment. Our experiment results demonstrate the high efficiency and effectiveness of our algorithms in real-life applications and datasets. Our multi-job allocation algorithm's end-to-end response time outperforms the other four baseline strategies by 33% to 63%.

关键词： edge-cloud computing edge AI workload allocation end-to-end response time

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ParalleX: An asynchronous execution model for scalable heterogeneous computing

ParalleX: An asynchronous execution model for scalable heter...

引用

21st international Parallel and Distributed Processing symposium, IPDPS 2007

作者： sterling, Thomas Department of Computer Science Center of Computation and Technology Louisiana State University Center for Advance Computation Research California Institute of Technology Computing and Computational Sciences Directorate Oak Ridge National Laboratory Department of Computer Science Louisiana State University California Institute of Technology Oak Ridge National Laboratory

ISBN: (纸本)1424409101

Heterogeneous system architecture has long been appreciated as a potential strategy for achieving super-linear speedup with respect to some normalizing parameter like number of nodes, cost, or power. However the challenge of programming and managing the system resources has been a limiting factor for the application of such systems on a broad scale. The exception has been the use of special purpose processors such as graphics processing units that may yield dramatic increases for such functionality enabling capabilities largely impossible otherwise like realistic high resolution real time interactive games. However, with power emerging as the dominant constraint on high performance computing and the need to make better use of logic and storage resources such components as the ClearSpeed SIMD attached processor and the IBM cell architectures among others is forcing mainstream computing to adopt heterogeneous processing. This keynote presentation will describe a computational model, ParalleX that provides an asynchronous runtime framework for supporting effective execution in an environment comprising heterogeneous elements. ParalleX is based on a message-driven split-phase multithreaded transaction processing paradigm synthesizing a number of concepts represented in prior art that in ensemble will facilitate management of heterogeneous resources and provide the basis for a systematic programming methodology. Also discussed in this presentation is another example of a heterogeneous architecture, Gilgamesh II, that provides separate mechanisms for computations that exhibit disparate locality properties. © 2007 IEEE.

关键词： Parallel processing systems

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Dynamically reconfigurable architecture for high-throughput hash function in key-value store 21

Dynamically reconfigurable architecture for high-throughput ...

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21st IEEE international Conference on high performance computing and Communications, 17th IEEE international Conference on Smart City and 5th IEEE international Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Liu, Ke Zhao, Mengying Ju, Lei Jia, Zhiping School of Computer Science and Technology Shandong University China School of Software Shandong University China

ISBN: (纸本)9781728120584

Distributed in-memory key-value stores (KVSs), such as Redis and Memcached, are widely deployed in modern data centers. In Redis, the throughput of Murmurhash2 is important to the performance of the whole KVS, and further affects the whole server. In this paper, we propose a high-performance scalable architecture for Murmurhash2 to improve data locality and reduce the latency, and deploy partial reconfiguration scheme to maximize the ratio of performance/area. Experiment results on Xilinx SOC platform demonstrate that the proposed architecture can improve the throughput by approximate 1.4x and achieve up to 5.3x performance/watt gain compared with the typical Intel platform. © 2019 IEEE.

关键词： Hash functions

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Using the Grid for enhancing the performance of a medical image search engine

Using the Grid for enhancing the performance of a medical im...

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21st IEEE international symposium on computer-Based Medical Systems

作者： Pitkanen, Mikko Juhani Zhou, Xin Hyvarinen, Antti E. J. Mueller, Henning Helsinki Inst Phys Technol Programme Helsinki Finland Univ Appl Sci Sierre Switzerland Aalto Univ Espoo Finland Univ & Hosp Geneva Geneva Switzerland

ISBN: (纸本)9780769531656

In this paper we show how Grid computing can be used to improve the operation of a medical image search system. The paper introduces the basic principles of a content-based image retrieval (CBIR) system and identifies the computationally challenging tasks in the system. For the computationally challenging tasks an efficient design is proposed that uses distributed Grid computing to carry out the image processing in a distributed and efficient way. The algorithms of the search system are executed by using a real medical image collection as input and a Grid computing infrastructure to provide the needed computing power. Finally, the results show how the image processing task that required tens of hours to complete can be processed by using only a fraction of the originally required computing time.

关键词： Grid computing

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Automatic Selection of Sparse Triangular Linear System Solvers on GPUs through Machine Learning Techniques 31

Automatic Selection of Sparse Triangular Linear System Solve...

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31st international symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Dufrechou, Ernesto Ezzatti, Pablo Quintana-Orti, Enrique S. Univ Republica Inst Comp Montevideo Uruguay Univ Politecn Valencia Dept Sistemas Informat Sistemas & Comp Valencia Spain

ISBN: (纸本)9781728141947

The solution of sparse triangular linear systems is often the most time-consuming stage of preconditioned iterative methods to solve general sparse linear systems, where it has to be applied several times for the same sparse matrix. For this reason, its computational performance has a strong impact on a wide range of scientific and engineering applications, which has motivated the study of its efficient execution on massively parallel platforms. In this sense, several methods have been proposed to tackle this operation on graphics processing units (GPUs), which can be classified under either the level-set or the self-scheduling paradigms. The results obtained from the experimental evaluation of the different methods suggest that both paradigms perform well for certain problems but poorly for others. Additionally, the relation between the properties of the linear systems and the performance of the different solvers is not evident a-priori. In this context, techniques that allow to predict inexpensively which is be the best solver for a particular linear system can lead to important runtime reductions. Our approach leverages machine learning techniques to select the best sparse triangular solver for a given linear system, with focus on the case where a small number of triangular systems has to be solved for the same matrix. We study the performance of several methods using different features derived from the sparse matrices, obtaining models with more than 80% of accuracy and acceptable prediction speed. These results are an important advance towards the automatic selection of the best GPU solver for a given sparse triangular linear system, and the characterization of the performance of these kernels.

关键词： graphics processors sparse triangular linear systems high performance machine learning

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Accelerating OpenSHMEM Collectives using In-Network computing Approach 31

Accelerating OpenSHMEM Collectives using In-Network Computin...

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31st international symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Venkata, Manjunath Gorentla Shainer, Gilad Graham, Richard L. Bloch, Gil Mellanox Technol Sunnyvale CA 94085 USA Mellanox Technol Yokneam IL USA

ISBN: (纸本)9781728141947

OpenSHMEM is one of the key programming models for high performance computing (HPC) applications with irregular communication patterns. Particularly, it is useful for problems that cannot be decomposed easily such as graph partitioning. The programming model supports Remote Memory Access (RMA), atomics, and collective operations. In this paper, we explore and evaluate the In-network computing approach for accelerating the OpenSHMEM collective operations, particularly barrier, broadcast, and reduction operations. To achieve acceleration, In-network computing leverages hardware engines on the networking elements and effective software that can efficiently use these capabilities. We explore the value of this approach for collective operations on the InfiniBand Host Channel Adapters (HCAs) and switches. Particularly, we focus on the recently introduced collective offload feature provided by the Mellanox Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)(TM) capability, which accelerates the barriers and reduction operations;the multicast capability accelerates the broadcast collective operation. To leverage the hardware capabilities, we complement it with an effective software stack that includes Hierarchical Collectives (HCOLL) library, and SHARP layer. Our evaluation on Oak Ridge National Laboratory (ORNL)'s Summit system, which is the fastest supercomputer on the June 2019 Top 500 list, show that the hardware and software acceleration in the In-network computing approach is key for achieving the performance and scalability required for collectives and applications. For a 5120 process OpenSHMEM job, our results show that the barrier operation is 710% faster, broadcast is 370% faster, and reduction operation is 10 times faster when compared with the implementation of collective operations with no acceleration. Further, experiments with a 2D-Heat kernel show that the In-network computing approach is very effective for real-world applications.

关键词： Parallel Programming Models Collective Operations HPC MPI InfiniBand

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Autonomous image segmentation by Competitive Unsupervised GrowCut 21

Autonomous image segmentation by Competitive Unsupervised Gr...

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21st international symposium on Symbolic and Numeric Algorithms for Scientific computing (SYNASC)

作者： Marginean, Radu Andreica, Anca Diosan, Laura Balint, Zoltan Babes Bolyai Univ Fac Math & Comp Sci Cluj Napoca Romania Cty Clin Emergency Hosp IMOGEN Res Inst Oradea Romania Babes Bolyai Univ Fac Phys Cluj Napoca Romania

ISBN: (纸本)9781728157245

In this paper, we introduce Competitive Unsupervised GrowCut, a cellular automata-based, unsupervised and autonomous algorithm that combines the label merging component of Unsupervised GrowCut with the soft label propagation mechanism of GrowCut. We evaluated our algorithm on two benchmark image segmentation datasets, along with two related methods proposed in the literature. We also provide a detailed comparative analysis of the three algorithms' segmentation performance and properties. Our analysis identified application-specific regimes that govern the relative performance of the analyzed algorithms.

关键词： image segmentation cellular automata computer vision

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A reconfigurable address translation cache for interconnect network of high performance computer 21

A reconfigurable address translation cache for interconnect ...

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作者： Zhang, Jianmin Li, Tiejun Xiao, Liquan School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781728120584

Most of users are accustomed to utilize the virtual address in their parallel programs running at the scalable high performance parallel computer systems. Therefore the virtual and physical address translation mechanism is necessary and crucial to bridge the hardware interface and software application. In this paper, a new reconfigurable address translation Cache (RATC) is proposed. The RATC employs the large capacity eDRAM (embedded Dynamic Random Access Memory) memory to meet the high hit ratio requirement. The capacity of RATC also can be configured four different sizes to reduce power dissipation. Many tests have been conducted on the real chip which implements the address translation architecture. The results show that the RATC has higher hit ratio than the three typical SRAM-based Cache designs, while running the well-known benchmarks. © 2019 IEEE.

关键词： Virtual addresses

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