The proceedings contain 57 papers. The topics discussed include: logical topology design with traffic grooming for scheduled demands;the impact of multi-core architectures on task retrieval policies for volunteer comp...
ISBN:
(纸本)9780889867741
The proceedings contain 57 papers. The topics discussed include: logical topology design with traffic grooming for scheduled demands;the impact of multi-core architectures on task retrieval policies for volunteer computing;evaluation of CPU power control and scheduling technique on parallelcomputing environment;clustered assignment of CCQS in information monitoring system supporting conjunctive queries;dense skip graphs as an overlay for P2P networks;multi-connection and multi-core aware all-gather on infiniband clusters;an improved prioritization method for task scheduling in heterogeneous computing systems;thread pipelining with clustered data communication on commodity CMPS;a distributed algorithm for disjoint paths in star networks;parallel algorithms for a visual text mining platform;and a cache miss analysis for multithreaded architectures.
As the scale of distributed training for Deep Neural Network (DNN) increases, communication has become a critical performance bottleneck in data center networks. In-Network Aggregation (INA) can accelerate aggregating...
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Cooperative inference for deep neural networks (DNNs) across edge devices has received increasing attention, due to the benefits of low latency, low power consumption, and privacy preservation. Cooperative inference p...
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With the advent of the era of big data, the traditional stand-alone computing model can no longer meet the needs of complex network analysis, and the distributedcomputing model provides a new solution. This paper fir...
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ISBN:
(数字)9798331536169
ISBN:
(纸本)9798331536176
With the advent of the era of big data, the traditional stand-alone computing model can no longer meet the needs of complex network analysis, and the distributedcomputing model provides a new solution. This paper first introduces the importance of complex network analysis and the challenges brought by big data, then discusses the application of distributedcomputing model in complex network analysis in detail, and focuses on the distributedcomputing algorithm based on MapReduce. Then, this paper puts forward an optimization strategy of distributed community detection algorithm based on modularity optimization. This strategy performs community detection tasks in parallel through MapReduce framework, and optimizes community division through iterative updating to maximize modularity. The experimental results show that the optimized algorithm has a significant improvement in computing time, accuracy and resource consumption. The distributedcomputing model based on MapReduce framework and its optimization strategy have obvious advantages in dealing with large-scale complex networks. It can not only improve the calculation efficiency and accuracy, but also reduce the resource consumption, which provides an efficient and feasible solution for complex network analysis.
The proceedings contain 110 papers. The topics discussed include: an evaluation of parallel algorithms on current memory consistency models;vertex-disjoint paths in transposition graphs;implementation of surface-to-su...
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The proceedings contain 110 papers. The topics discussed include: an evaluation of parallel algorithms on current memory consistency models;vertex-disjoint paths in transposition graphs;implementation of surface-to-surface contact algorithms using iterative solvers on parallel computers;wavelength assignment for broadcasting in sparse conversion tree networks with fewest converters;a two-phase scheduling approach for grid computing;parallelization of multimedia applications on the multi-level computing architecture;co-operation of processes in parallel simulated annealing;model-based design of the communication system in an integrated architecture;improved asynchronous group mutual exclusion in token-passing networks;global static-priority scheduling of sporadic task systems on multiprocessor platforms;approximating the buffer allocation problem using epochs;and entropy properties in program behaviors and branch predictors.
When all the qubits needed for solving a problem are not located in a single quantum computer, qubits from different quantum computers can be collectively utilized. In this case, quantum communication is needed for th...
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ISBN:
(数字)9798331531591
ISBN:
(纸本)9798331531607
When all the qubits needed for solving a problem are not located in a single quantum computer, qubits from different quantum computers can be collectively utilized. In this case, quantum communication is needed for the multiple quantum computers to communicate with each other. Several studies address the problem of minimizing the number of quantum communications when evaluating a general quantum circuit. The solutions proposed typically involve solving some intractable problems. In this paper, we show that we can obtain much better solutions when we focus on solving specific problems (instead of seeking solutions for generic circuits). Specifically, we consider several fundamental quantum circuits and identify communication protocols that need a much smaller number of communication steps than those offered by generic solutions. Our work is in line with traditional parallel and distributedcomputing research where typically scientists focus on solving specific problems (such as sorting, matrix multiplication, network flow, etc.) in a parallel or distributed setting.
The proceedings contain 52 papers. The topics discussed include: performance and reliability evaluations on stochastic activity networks;multi-path position-based routing in mobile ad-hoc networks;PARA-SNOR : a multi-...
ISBN:
(纸本)9780889868113
The proceedings contain 52 papers. The topics discussed include: performance and reliability evaluations on stochastic activity networks;multi-path position-based routing in mobile ad-hoc networks;PARA-SNOR : a multi-thread snort on multi-core ia platform;on the surface area of the alternating group networks;a programming model for high-performance adaptive applications on pervasive mobile grids;dynamic P2P topology management for scalable H.264 multiple-description coded video streaming;a new protocol to optimize the degree of concurrency in object-oriented databases;highly parallel multi-dimensional fast fourier transform on fine- and coarse-grained many-core approaches;cache-based bounds checking for multi-threaded c programs;design and evaluation of a user-oriented availability benchmark for distributed file systems;and partitioning strategies: spatiotemporal patterns of program decomposition.
Existing research on multi-component radar signal recognition widely adopts recognition frameworks based on time-frequency transformation (TFT) and convolutional neural networks (CNN). To address the issue of the...
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The proceedings contain 30 papers. The topics discussed include: an efficient out-of-core implementation of Block Cholesky decomposition on a multi-GPU system;a methodology for characterizing applications for cloud co...
The proceedings contain 30 papers. The topics discussed include: an efficient out-of-core implementation of Block Cholesky decomposition on a multi-GPU system;a methodology for characterizing applications for cloud computing environments;social welfare maximization oriented admissions control over delay-sensitive media streams;a software architecture for distributed volume rendering on HPC systems;duty-cycle scheduling for intruder detection in wireless sensor networks;many-core acceleration of vertical plane radio wave propagation prediction;an autonomic detection and protection system for denial of service attack;supporting dynamic data structures in a shared-memory based GPGPU programming framework;improving the performance of document classification by using GPU parallelism;folded Torus based power aware interconnection topology for high-performance multicore architecture;and a power-aware versatile victim cache to reduce average memory latency in parallel architectures.
Due to the limited resources of edge networks, the heterogeneity of user content requests, high-cost caching from direct resource hits, and redundancy in resource retention time hinder system performance. Traditional ...
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