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检索条件"任意字段=13th IEEE International Symposium on Parallel and Distributed Processing with Applications"

共 2574 条记录，以下是121-130 订阅

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PIPArch: Programmable Image processing Architecture Using Sliding Array 19

PIPArch: Programmable Image Processing Architecture Using Sl...

引用

19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Wu, Feiyang Song, Zhuoran Ke, Jing Jiang, Li Jing, Naifeng Liang, Xiaoyao Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781665435741

Image processing arises as a promising domain for manifold applications requiring for heavy computing power and memory bandwidth with higher image resolution. Graphics processing unit (GPU) is widely used in image processing algorithms but suffers from its powerful programmability that costs high hardware overhead. Moreover, GPU consumes much energy to access data from high-capacity register files, making it hard to implement on wearable devices. Enabling low power and efficient architecture with low hardware overhead remains challenging. In this paper, we propose a programmable image processing architecture (PIPArch) that explores the spatial locality in images to save energy while achieving high performance. We also design the instruction set architecture (ISA) to control the PIPArch. By supporting multiple parallel pipelines, we can keep the hardware utilization of PIPArch high. We evaluate the proposed PIPArch by developing the cycle-accurate simulator with some typical image processing algorithms. Compared to NVIDIA Tesla V100 GPU, PIPArch gains 23.63x speedup.

关键词： Image processing domain-specific architecture instruction set architecture

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Fast GPU Implementation of Dumer's Algorithm Solving the Syndrome Decoding Problem 19

Fast GPU Implementation of Dumer's Algorithm Solving the Syn...

引用

19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Narisada, Shintaro Fukushima, Kazuhide Kiyomoto, Shinsaku KDDI Res Inc Fujimino Japan

ISBN: (纸本)9781665435741

the syndrome decoding problem (SDP) is a security basis for code-based cryptographic algorithms such as the McEliece cryptosystem, which has been selected as a finalist for the third round of the post-quantum cryptography (PQC) standardization project organized by the National Institute of Standards and Technology (NIST). Information set decoding (ISD) is a general term for algorithms that efficiently solve SDPs based on combinatorial enumeration. In this paper, we focused on Dumer's algorithm, which is relatively lightweight among the ISD algorithms;and proposed a multi-parallel Dumer's algorithm optimized for computing devices that perform massively parallel processing. We implemented our algorithm on a GPU and tested it by solving some SDP instances in the decoding challenge hosted by Inria. We report that our algorithm first solved a 1161-dimensional SDP in the Goppa-McEliece setting in approximately 380 hours, which has a computational complexity of 2(52). Our results contribute to a more detailed security analysis for code-based cryptosystems.

关键词： Terms Syndrome Decoding Information Set Decoding Graphics processing Unit (GPU)

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Optimization of key and value storage in vector map rendering 19

Optimization of key and value storage in vector map renderin...

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19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Jing, Feng Jiang, Ying Chen, Shengbin Lv, Xirong He, Jiayi State Grid Shanxi Elect Power Co Taiyuan Peoples R China Xiamen Great Power Geo Informat Technol Co Ltd Xiamen Peoples R China Xiamen Univ Technol Sch Comp & Informat Engn Xiamen Peoples R China

ISBN: (纸本)9781665435741

With the continuous development of data-centric Internet applications, data management solutions for massive data have gradually become a new research direction in vector map rendering. the introduction of distributed "cloud computing" technology has continuously improved the storage and management of massive data. Supported by a large number of distributed systems, it provides normal services to the outside world, as well as fast data location services. the system has good performance in data storage efficiency, node load balancing and system scalability, and has an efficient data positioning mechanism. the basic idea of the system design draws on the hash algorithm and the consistent hash algorithm, and improves the scalability of the basic algorithm. through the three-tier layout: front-end server layer, consistent hash layer, data node layer, to maximize the consistency, availability, efficiency and scalability of the system.

关键词： distributed storage system consistent hash key value

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COPA Use Case: distributed Secure Joint Computation 30

COPA Use Case: Distributed Secure Joint Computation

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ieee 30th international symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Patel, Rushi Haghi, Pouya Jain, Shweta Kot, Andriy Krishnan, Venkata Varia, Mayank Herbordt, Martin Boston Univ Coll Engn Boston MA 02215 USA Boston Univ Comp & Data Sci Boston MA 02215 USA Intel Corp Hudson MA USA

ISBN: (纸本)9781665483322

Data centers provide good environments for distributed computing as they are easily accessible and may have low-latency communication between nodes [1] ; often, however, performance is limited by network bandwidth. these network bottlenecks drive the need for alternative communication resources to improve performance of large-scale applications. SmartNICs [2] – [4] have been introduced to perform the same tasks of standard NICs, but contain additional resources to allow for network function optimization with additional hardware. Adoption of SmartNICs continues to increase as a means to accelerate network functions and offload packet processing tasks away from CPU resources [5] – [13] .

关键词： Data centers Bandwidth Hardware Task analysis Low latency communication distributed computing Standards

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An Efficient Scheduling Algorithm for distributed Heterogeneous Systems with Task Duplication Allowed 19

An Efficient Scheduling Algorithm for Distributed Heterogene...

引用

19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Shi, Hao Chen, Yixiang Xu, Jinyi East China Normal Univ Dept Embedded Software & Syst Software Engn Inst Shanghai Peoples R China MOE Engn Res Ctr Software Hardware Codesign Techn Shanghai Peoples R China

ISBN: (纸本)9781665435741

Due to the restriction of data transmission speed and bandwidth between processing units, communication delays are significantly impacting the complete time of applications in distributed heterogeneous systems. Most of the existing methods considering communication delays are approximate. the performance of solutions given by these heuristic methods are depends on the input instance. Another common choice is optimization methods, such as genetic algorithms and branch-and-bound search. However, these algorithms solve general situations with low efficiency. this paper proposes an efficient scheduling algorithm with task duplication allowed, called DHSA. Task duplication allows tasks executing multiple times to trade extra computation for communication delays, which is widely used in parallel system scheduling approximately. To address the complexity explosion caused by allowing all-task duplication, we decompose the scheduling into allocation and ordering stages and optimize them separately with solving optimization model and slack-interval aware ordering approach. Extensive experiments show that DHSA gives more optimal solutions within feasible time comparing to two state-of-the-art methods, particularly for applications with medium to high communication load.

关键词： Duplication-based Scheduling distributed Heterogeneous Systems Optimization Model Slack-interval Aware Ordering

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parallel analysis of TACLeBench kernel benchmark's loop and procedure level speculation 19

Parallel analysis of TACLeBench kernel benchmark's loop and ...

引用

19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Meng, Huiling Wang, Yaobin Li, Ling Musariri, Manasah Wang, Xinyi Southwest Univ Sci & Technol Mianyang Sichuan Peoples R China

ISBN: (纸本)9781665435741

thread level speculation (TLS) technology has gained substantial international recognition due to its unique parallel program execution. It uses the potential parallel execution of programs to improve the utilization of multi-core resources. However, the kernel benchmark of TACLeBench has not effectively been analyzed using TLS parallelization. In response to this problem, we select 7 programs in the TACLeBench kernel benchmark to analyze its loop and procedure level speculation executions to measure their maximum potential parallelism. Furthermore, we discuss its runtime characteristics (thread size, speculative parallelism coverage, dependency feature) and the influence of program source code on speedup. Our experimental results illustrate that: 1) Most applications achieve impressive results. Bsort reaches 20.79x speedup in loop level speculation, and lms has 9.51x speedup in procedure level speculation;2) By using TLS to accelerate the TACLeBench kernel benchmark, most applications effectively utilize computing resources from 4 to 16 cores;3) the kernel benchmark is more suitable for developing parallelism in loop level speculation.

关键词： thread level speculation (TLS) multi-core TACLeBench

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parallel Counterfactual Regret Minimization in Crowdsourcing Imperfect-information Expanded Game 19

Parallel Counterfactual Regret Minimization in Crowdsourcing...

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19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Zhang, Jie Li, Kefan Zhang, Baoming Xu, Ming Wang, Chongjun Nanjing Univ State Key Lab Novel Software Technol Dept Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665435741

Counterfactual regret minimization (CFR) is one of the most widely used algorithms in iterative optimization algorithms. It is used to solve complex imperfect-information game problems. this paper introduced the Global Counterfactual Regret Minimization Local Update (GCFR+) to solve task planning problems in a crowdsourcing environment. We designed a parallel mechanism to alleviate possible parallel conflicts in actual crowdsourcing scenarios and increase personal rewards. First of all, we chose to test the performance of GCFR+ on data sets with different scales. then we compared the result with the result of the decision model with a parallel mechanism. It can be seen that the parallel mechanism has significantly improved the efficiency of the decision model. Finally, unlike general CFR, we proved that GCFR+ is applicable to decision tree pruning of imperfect-information games.

关键词： Crowdsourcing task planning Counterfactual regret minimization parallel mechanisms dynamic gaming

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Parameter Servers Placement for distributed Deep Learning in Edge computing 19

Parameter Servers Placement for Distributed Deep Learning in...

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19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Yan, Jiaquan Wu, Yalan Wu, Jigang Chen, Long Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9781665435741

the architecture based on global-local parameter servers (PSs) is an appealing framework to accelerate the training of the global model in distributed deep learning. However, the accuracy and training time are influenced by the placement strategy of PSs in distributed deep learning, due to the adopted gradient descent methods and the heterogeneous computation and communication resources. therefore, this paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training time of the distributed deep learning under the constraints of the storage capacity and the number of local PSs. then, we prove the NP-hardness of the proposed problem. To solve the problem, a parameter servers placement algorithm, followed by an adjustment algorithm, is proposed in this paper, by continuously making decisions for placement strategy of PSs to decrease the training time of the global model. Simulation results show that, the proposed combined algorithm performs better than existing works for all cases, in terms of the training time of global model. Moreover, the performance of the proposed algorithm is nearly close to that of brute-force approach, in terms of the training time of the global model.

关键词： Edge Computing distributed Deep Learning Parameter Server Placement

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An efficient hybrid MPI/OpenMP parallelization of the asynchronous ADMM algorithm 19

An efficient hybrid MPI/OpenMP parallelization of the asynch...

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19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Qiu, Qinnan Lei, Yongmei Wang, Dongxia Wang, Guozheng Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China

ISBN: (纸本)9781665435741

Alternating direction method of multipliers (ADMM) is an efficient algorithm to solve large- scale machine learning problems in a distributed environment. To make full use of the hierarchical memory model in modern highperformance computing systems, this paper implements a hybrid MPI/OpenMP parallelization of the asynchronous ADMM algorithm (AH-ADMM). the AH-ADMM algorithm updates local variables in parallel by OpenMP threads and exchanges information between MPI processes, which relieves memory and communication pressure by replacing multiprocessing with multi- threading. Furthermore, for the SVM problem, the AH-ADMMalgorithm speeds up the calculation of sub- problems through an efficient parallel optimization strategy. this paper effectively combines the features of both algorithm design and programming model. Experiments on the Ziqiang4000 high-performance cluster demonstrate that the AH- ADMM algorithm scales better and run faster than the existing distributed ADMM algorithms implemented by pure MPI. the AH-ADMM can reduce the communication overhead by up to 91.8% and increase the convergence rate by up to 36x. For large datasets, the AH-ADMM can scale well on the cluster which over 129 cores.

关键词： distributed ADMM algorithm asynchronous communication hybrid parallel programming model MPI OpenMP

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A distributed Computation Offloading Scheduling Framework based on Deep Reinforcement Learning 19

A Distributed Computation Offloading Scheduling Framework ba...

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19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Dai, Bin Ren, Tao Niu, Jianwei Hu, Zheyuan Hu, Shucheng Qiu, Meikang Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Zhengzhou Univ Res Inst Ind Technol Sch Informat Engn Zhengzhou 450001 Peoples R China Texas A&M Univ Commerce Dept Comp Sci Commerce TX 75428 USA

ISBN: (纸本)9781665435741

Recent years have witnessed the rapid growth of smart devices and mobile applications. However, mobile applications are typically computation-intensive and delay-sensitive, while User Devices (UDs) are usually resource-limited. Mobile Edge Computing (MEC) has been proposed as a promising paradigm to mitigate the tension, where UDs' tasks could be executed either locally on itself or remotely on the edge server via computation offloading. Lots of efficient computation offloading scheduling approaches have been proposed, whereas most of them are based on centralized scheduling which could face troubles in large-scale MEC. To address the issue, this paper proposes a distributed scheduling framework by leveraging the idea of `centralized training and distributed scheduling'. Furthermore, the Actor-Critic reinforcement learning is adopted to build the framework where the Actor and Critic play the roles of distributed scheduling and centralized training, respectively. Extensive simulations are conducted and the experimental results verify the effectiveness and efficiency of the proposed framework.

关键词： distributed scheduling computation offloading deep reinforcement learning mobile edge computing

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