Parallel Process. Lett.最新文献_第3页

Machine Learning to Design an Auto-tuning System for the Best Compressed Format Detection for Parallel Sparse Computations 机器学习设计一种自动调谐系统，用于并行稀疏计算的最佳压缩格式检测

Parallel Process. Lett. Pub Date : 2021-11-16 DOI: 10.1142/s0129626421500195

O. Hamdi-Larbi, Ichrak Mehrez, T. Dufaud

{"title":"Machine Learning to Design an Auto-tuning System for the Best Compressed Format Detection for Parallel Sparse Computations","authors":"O. Hamdi-Larbi, Ichrak Mehrez, T. Dufaud","doi":"10.1142/s0129626421500195","DOIUrl":"https://doi.org/10.1142/s0129626421500195","url":null,"abstract":"Many applications in scientific computing process very large sparse matrices on parallel architectures. The presented work in this paper is a part of a project where our general aim is to develop an auto-tuner system for the selection of the best matrix compression format in the context of high-performance computing. The target smart system can automatically select the best compression format for a given sparse matrix, a numerical method processing this matrix, a parallel programming model and a target architecture. Hence, this paper describes the design and implementation of the proposed concept. We consider a case study consisting of a numerical method reduced to the sparse matrix vector product (SpMV), some compression formats, the data parallel as a programming model and, a distributed multi-core platform as a target architecture. This study allows extracting a set of important novel metrics and parameters which are relative to the considered programming model. Our metrics are used as input to a machine-learning algorithm to predict the best matrix compression format. An experimental study targeting a distributed multi-core platform and processing random and real-world matrices shows that our system can improve in average up to 7% the accuracy of the machine learning.","PeriodicalId":422436,"journal":{"name":"Parallel Process. Lett.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124163348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond Rings: Gathering in 1-Interval Connected Graphs 超越环:1区间连通图的聚集

Parallel Process. Lett. Pub Date : 2021-11-11 DOI: 10.1142/s0129626421500201

O. Michail, P. Spirakis, Michail Theofilatos

{"title":"Beyond Rings: Gathering in 1-Interval Connected Graphs","authors":"O. Michail, P. Spirakis, Michail Theofilatos","doi":"10.1142/s0129626421500201","DOIUrl":"https://doi.org/10.1142/s0129626421500201","url":null,"abstract":"We examine the problem of gathering [Formula: see text] agents (or multi-agent rendezvous) in dynamic graphs which may change in every round. We consider a variant of the [Formula: see text]-interval connectivity model [9] in which all instances (snapshots) are always connected spanning subgraphs of an underlying graph, not necessarily a clique. The agents are identical and not equipped with explicit communication capabilities, and are initially arbitrarily positioned on the graph. The problem is for the agents to gather at the same node, not fixed in advance. We first show that the problem becomes impossible to solve if the underlying graph has a cycle. In light of this, we study a relaxed version of this problem, called weak gathering, where the agents are allowed to gather either at the same node, or at two adjacent nodes. Our goal is to characterize the class of 1-interval connected graphs and initial configurations in which the problem is solvable, both with and without homebases. On the negative side we show that when the underlying graph contains a spanning bicyclic subgraph and satisfies an additional connectivity property, weak gathering is unsolvable, thus we concentrate mainly on unicyclic graphs. As we show, in most instances of initial agent configurations, the agents must meet on the cycle. This adds an additional difficulty to the problem, as they need to explore the graph and recognize the nodes that form the cycle. We provide a deterministic algorithm for the solvable cases of this problem that runs in [Formula: see text] number of rounds.","PeriodicalId":422436,"journal":{"name":"Parallel Process. Lett.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132521463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability Evaluation of Bicube-Based Multiprocessor System under the g-Good-Neighbor Restriction 基于双立方体的多处理机系统在g-近邻约束下的可靠性评估

Parallel Process. Lett. Pub Date : 2021-11-11 DOI: 10.1142/s0129626421500183

Jiafei Liu, Shuming Zhou, E. Cheng, Gaolin Chen, Min Li

引用次数: 1

Abnormal Quantum State Search Based on Parallel Phase Comparison 基于并行相位比较的异常量子态搜索

Parallel Process. Lett. Pub Date : 2021-11-09 DOI: 10.1142/s0129626421500225

Guanlei Xu, Xiaogang Xu, Xiaotong Wang

引用次数: 0

Fault Detection Method of CNC Machine Tool Based on Wavelet Transform 基于小波变换的数控机床故障检测方法

Parallel Process. Lett. Pub Date : 2021-10-20 DOI: 10.1142/s0129626421410012

Junying Liu

引用次数: 0

Design and Implementation of Low Power and Area Efficient Architecture for High Performance ALU 高性能ALU低功耗、高效率架构的设计与实现

Parallel Process. Lett. Pub Date : 2021-10-19 DOI: 10.1142/s0129626421500171

U. Penchalaiah, V. S. Kumar

{"title":"Design and Implementation of Low Power and Area Efficient Architecture for High Performance ALU","authors":"U. Penchalaiah, V. S. Kumar","doi":"10.1142/s0129626421500171","DOIUrl":"https://doi.org/10.1142/s0129626421500171","url":null,"abstract":"Digital Signal Processors (DSP) have a ubiquitous presence in almost all civil and military signal processing applications, including mission critical environments like nuclear reactors, process control etc. Arithmetic and Logic units (ALU), being the heart of any digital signal processor, play critical and decisive roles in achieving the required parameter benchmarks and the overall efficiency and robustness of the digital signal processor. State of the art research has shown successful traction with the performance requirements of critical Multiply-Accumulate (MAC) parameters, like reduced power consumption, small electronic real estate footprint and reduction in delay with the associated design complexity. Judicious placement of its building blocks, namely, the truncated multiplier and half-sum carry generation-sum carry generation (HSCG-SCG) adder in the architectural design of ALU and the type of adder and multiplier circuits selected are the core decisions that decide the overall performance of the ALU. To overcome the drawback and to improve the performance further, this work proposes a new architecture for the square root (SQRT) carry select adder (CSLA) using half-sum generation (HSG), half-carry generation (HCG), full-sum generation (FSG) and full-carry generation (FCG) blocks. The proposed design contains N-bit architecture, and comparative results are considered for 8-bit, 16-bit and 32-bit combinations. All the designs are implemented in the Xilinx ISE environment and the results show that better area, power, and delay performance compared to the state of art methods.","PeriodicalId":422436,"journal":{"name":"Parallel Process. Lett.","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125895329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Efficient Non-Parametric Statistical Test for Assessing Some Treatment Methods of Clinical Data 一种评估临床资料处理方法的有效非参数统计检验

Parallel Process. Lett. Pub Date : 2021-09-24 DOI: 10.1142/s0129626421420019

Mahmoud Mansour, Mohamed Aboshady

引用次数: 0

Parallel Network Analysis and Communities Detection (PANC) Pipeline for the Analysis and Visualization of COVID-19 Data 面向COVID-19数据分析和可视化的并行网络分析和社区检测(PANC)管道

Parallel Process. Lett. Pub Date : 2021-09-22 DOI: 10.1142/s0129626421420020

Giuseppe Agapito, Marianna Milano, M. Cannataro

{"title":"Parallel Network Analysis and Communities Detection (PANC) Pipeline for the Analysis and Visualization of COVID-19 Data","authors":"Giuseppe Agapito, Marianna Milano, M. Cannataro","doi":"10.1142/s0129626421420020","DOIUrl":"https://doi.org/10.1142/s0129626421420020","url":null,"abstract":"A new coronavirus, causing a severe acute respiratory syndrome (COVID-19), was started at Wuhan, China, in December 2019. The epidemic has rapidly spread across the world becoming a pandemic that, as of today, has affected more than 70 million people causing over 2 million deaths. To better understand the evolution of spread of the COVID-19 pandemic, we developed PANC (Parallel Network Analysis and Communities Detection), a new parallel preprocessing methodology for network-based analysis and communities detection on Italian COVID-19 data. The goal of the methodology is to analyze set of homogeneous datasets (i.e. COVID-19 data in several regions) using a statistical test to find similar/dissimilar behaviours, mapping such similarity information on a graph and then using community detection algorithm to visualize and analyze the initial dataset. The methodology includes the following steps: (i) a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data; (ii) an effective workload balancing function to improve performance; (iii) the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships; (iv) the discovering and visualization of communities of regions that show similar behaviour. The methodology is general and can be applied to world-wide data about COVID-19, as well as to all types of data sets in tabular and matrix format. To estimate the scalability with increasing workloads, we analyzed three synthetic COVID-19 datasets with the size of 90.0[Formula: see text]MB, 180.0[Formula: see text]MB, and 360.0[Formula: see text]MB. Experiments was performed on showing the amount of data that can be analyzed in a given amount of time increases almost linearly with the number of computing resources available. Instead, to perform communities detection, we employed the real data set.","PeriodicalId":422436,"journal":{"name":"Parallel Process. Lett.","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133715858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

On the 3-Extra Connectivity of Enhanced Hypercubes 增强型超立方体的3-Extra连通性

Parallel Process. Lett. Pub Date : 2021-09-16 DOI: 10.1142/s012962642150016x

Liyang Zhai, Liqiong Xu, Shanshan Yin

引用次数: 0

OpenMP Implementation of Parallel Longest Common Subsequence Algorithm for Mathematical Expression Retrieval 数学表达式检索并行最长公共子序列算法的OpenMP实现

Parallel Process. Lett. Pub Date : 2021-06-01 DOI: 10.1142/S0129626421500079

Pavan Kumar Perepu

{"title":"OpenMP Implementation of Parallel Longest Common Subsequence Algorithm for Mathematical Expression Retrieval","authors":"Pavan Kumar Perepu","doi":"10.1142/S0129626421500079","DOIUrl":"https://doi.org/10.1142/S0129626421500079","url":null,"abstract":"Given a mathematical expression in LaTeX or MathML format, retrieval algorithm extracts similar expressions from a database. In our previous work, we have used Longest Common Subsequence (LCS) algorithm to match two expressions of lengths, [Formula: see text] and [Formula: see text], which takes [Formula: see text] time complexity. If there are [Formula: see text] database expressions, total complexity is [Formula: see text], and an increase in [Formula: see text] also increases this complexity. In the present work, we propose to use parallel LCS algorithm in our retrieval process. Parallel LCS has [Formula: see text] time complexity with [Formula: see text] processors and total complexity can be reduced to [Formula: see text]. For our experimentation, OpenMP based implementation has been used on Intel [Formula: see text] processor with 4 cores. However, for smaller expressions, parallel version takes more time as the implementation overhead dominates the algorithmic improvement. As such, we have proposed to use parallel version, selectively, only on larger expressions, in our retrieval algorithm to achieve better performance. We have compared the sequential and parallel versions of our ME retrieval algorithm, and the performance results have been reported on a database of 829 mathematical expressions.","PeriodicalId":422436,"journal":{"name":"Parallel Process. Lett.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121700870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2