J. Inf. Process. Syst.最新文献_第3页

New Two-Level L1 Data Cache Bypassing Technique for High Performance GPUs 一种新的高性能gpu双级L1数据缓存绕过技术

J. Inf. Process. Syst. Pub Date : 2021-02-09 DOI: 10.3745/JIPS.01.0062

Gwang Bok Kim, C. Kim

{"title":"New Two-Level L1 Data Cache Bypassing Technique for High Performance GPUs","authors":"Gwang Bok Kim, C. Kim","doi":"10.3745/JIPS.01.0062","DOIUrl":"https://doi.org/10.3745/JIPS.01.0062","url":null,"abstract":"On-chip caches of graphics processing units (GPUs) have contributed to improved GPU performance by reducing long memory access latency. However, cache efficiency remains low despite the facts that recent GPUs have considerably mitigated the bottleneck problem of L1 data cache. Although the cache miss rate is a reasonable metric for cache efficiency, it is not necessarily proportional to GPU performance. In this study, we introduce a second key determinant to overcome the problem of predicting the performance gains from L1 data cache based on the assumption that miss rate only is not accurate. The proposed technique estimates the benefits of the cache by measuring the balance between cache efficiency and throughput. The throughput of the cache is predicted based on the warp occupancy information in the warp pool. Then, the warp occupancy is used for a second bypass phase when workloads show an ambiguous miss rate. In our proposed architecture, the L1 data cache is turned off for a long period when the warp occupancy is not high. Our two-level bypassing technique can be applied to recent GPU models and improves the performance by 6% on average compared to the architecture without bypassing. Moreover, it outperforms the conventional bottleneck-based bypassing techniques.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132065838","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

The Future of Quantum Information: Challenges and Vision 量子信息的未来:挑战与展望

J. Inf. Process. Syst. Pub Date : 2021-02-09 DOI: 10.3745/JIPS.01.0063

Dohyun Kim, Jungho Kang, Tae Woo Kim, Yi Pan, Jong Hyuk Park

引用次数: 5

Re-SSS: Rebalancing Imbalanced Data Using Safe Sample Screening Re-SSS:使用安全样本筛选重新平衡不平衡数据

J. Inf. Process. Syst. Pub Date : 2021-02-09 DOI: 10.3745/JIPS.01.0065

Hongbo Shi, Xin Chen, Mingzhe Guo

引用次数: 3

On-Demand Remote Software Code Execution Unit Using On-Chip Flash Memory Cloudification for IoT Environment Acceleration 使用片上闪存云化实现物联网环境加速的按需远程软件代码执行单元

J. Inf. Process. Syst. Pub Date : 2021-02-09 DOI: 10.3745/JIPS.01.0064

Dongkyu Lee, Moon Gi Seok, Daejin Park

{"title":"On-Demand Remote Software Code Execution Unit Using On-Chip Flash Memory Cloudification for IoT Environment Acceleration","authors":"Dongkyu Lee, Moon Gi Seok, Daejin Park","doi":"10.3745/JIPS.01.0064","DOIUrl":"https://doi.org/10.3745/JIPS.01.0064","url":null,"abstract":"In an Internet of Things (IoT)-configured system, each device executes on-chip software. Recent IoT devices require fast execution time of complex services, such as analyzing a large amount of data, while maintaining low-power computation. As service complexity increases, the service requires high-performance computing and more space for embedded space. However, the low performance of IoT edge devices and their small memory size can hinder the complex and diverse operations of IoT services. In this paper, we propose a remote on-demand software code execution unit using the cloudification of on-chip code memory to accelerate the program execution of an IoT edge device with a low-performance processor. We propose a simulation approach to distribute remote code executed on the server side and on the edge side according to the program’s computational and communicational needs. Our on-demand remote code execution unit simulation platform, which includes an instruction set simulator based on 16-bit ARM Thumb instruction set architecture, successfully emulates the architectural behavior of on-chip flash memory, enabling embedded devices to accelerate and execute software using remote execution code in the IoT environment.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130368222","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

Assisted Magnetic Resonance Imaging Diagnosis for Alzheimer's Disease Based on Kernel Principal Component Analysis and Supervised Classification Schemes 基于核主成分分析和监督分类方案的阿尔茨海默病磁共振成像辅助诊断

J. Inf. Process. Syst. Pub Date : 2021-02-09 DOI: 10.3745/JIPS.04.0204

Yu Wang, Wenbin Zhou, Chongchong Yu, Weijun Su

{"title":"Assisted Magnetic Resonance Imaging Diagnosis for Alzheimer's Disease Based on Kernel Principal Component Analysis and Supervised Classification Schemes","authors":"Yu Wang, Wenbin Zhou, Chongchong Yu, Weijun Su","doi":"10.3745/JIPS.04.0204","DOIUrl":"https://doi.org/10.3745/JIPS.04.0204","url":null,"abstract":"Alzheimer’s disease (AD) is an insidious and degenerative neurological disease. It is a new topic for AD patients to use magnetic resonance imaging (MRI) and computer technology and is gradually explored at present. Preprocessing and correlation analysis on MRI data are firstly made in this paper. Then kernel principal component analysis (KPCA) is used to extract features of brain gray matter images. Finally supervised classification schemes such as AdaBoost algorithm and support vector machine algorithm are used to classify the above features. Experimental results by means of AD program Alzheimer’s Disease Neuroimaging Initiative (ADNI) database which contains brain structural MRI (sMRI) of 116 AD patients, 116 patients with mild cognitive impairment, and 117 normal controls show that the proposed method can effectively assist the diagnosis and analysis of AD. Compared with principal component analysis (PCA) method, all classification results on KPCA are improved by 2%–6% among which the best result can reach 84%. It indicates that KPCA algorithm for feature extraction is more abundant and complete than PCA.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"435 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125800966","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

An Energy Efficient Multi-hop Cluster-Head Election Strategy for Wireless Sensor Networks 一种高效的无线传感器网络多跳簇头选举策略

J. Inf. Process. Syst. Pub Date : 2021-02-09 DOI: 10.3745/JIPS.04.0202

Liquan Zhao, Shuai Guo

引用次数: 0

Development of a Targeted Recommendation Model for Earthquake Risk Prevention in the Whole Disaster Chain 全灾害链地震风险防范针对性推荐模型的建立

J. Inf. Process. Syst. Pub Date : 2021-02-09 DOI: 10.3745/JIPS.04.0201

Xiaohui Su, Ke Ming, Xiaodong Zhang, Junming Liu, Da Lei

{"title":"Development of a Targeted Recommendation Model for Earthquake Risk Prevention in the Whole Disaster Chain","authors":"Xiaohui Su, Ke Ming, Xiaodong Zhang, Junming Liu, Da Lei","doi":"10.3745/JIPS.04.0201","DOIUrl":"https://doi.org/10.3745/JIPS.04.0201","url":null,"abstract":"Strong earthquakes have caused substantial losses in recent years, and earthquake risk prevention has aroused a significant amount of attention. Earthquake risk prevention products can help improve the self and mutualrescue abilities of people, and can create convenient conditions for earthquake relief and reconstruction work. At present, it is difficult for earthquake risk prevention information systems to meet the information requirements of multiple scenarios, as they are highly specialized. Aiming at mitigating this shortcoming, this study investigates and analyzes four user roles (government users, public users, social force users, insurance market users), and summarizes their requirements for earthquake risk prevention products in the whole disaster chain, which comprises three scenarios (pre-quake preparedness, in-quake warning, and post-quake relief). A targeted recommendation rule base is then constructed based on the case analysis method. Considering the user’s location, the earthquake magnitude, and the time that has passed since the earthquake occurred, a targeted recommendation model is built. Finally, an Android APP is implemented to realize the developed model. The APP can recommend multi-form earthquake risk prevention products to users according to their requirements under the three scenarios. Taking the 2019 Lushan earthquake as an example, the APP exhibits that the model can transfer real-time information to everyone to reduce the damage caused by an earthquake.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127976320","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: 1

Stagewise Weak Orthogonal Matching Pursuit Algorithm Based on Adaptive Weak Threshold and Arithmetic Mean 基于自适应弱阈值和算术均值的分阶段弱正交匹配追踪算法

J. Inf. Process. Syst. Pub Date : 2020-12-01 DOI: 10.3745/JIPS.03.0152

Liquan Zhao, Ke Ma

引用次数: 5

Directional Interpolation Based on Improved Adaptive Residual Interpolation for Image Demosaicking 基于改进自适应残差插值的图像去马赛克方向插值

J. Inf. Process. Syst. Pub Date : 2020-12-01 DOI: 10.3745/JIPS.02.0148

Chenbo Liu

引用次数: 1

Big IoT Healthcare Data Analytics Framework Based on Fog and Cloud Computing 基于雾和云计算的大物联网医疗数据分析框架

J. Inf. Process. Syst. Pub Date : 2020-12-01 DOI: 10.3745/JIPS.04.0193

Hamoud H. Alshammari, Sameh Abd El-Ghany, and Abdulaziz Shehab

{"title":"Big IoT Healthcare Data Analytics Framework Based on Fog and Cloud Computing","authors":"Hamoud H. Alshammari, Sameh Abd El-Ghany, and Abdulaziz Shehab","doi":"10.3745/JIPS.04.0193","DOIUrl":"https://doi.org/10.3745/JIPS.04.0193","url":null,"abstract":"Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.","PeriodicalId":415161,"journal":{"name":"J. Inf. Process. Syst.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115372109","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: 17