Concurrency and Computation-Practice & Experience最新文献

{"title":"Measuring Thread Timing to Assess the Feasibility of Early-Bird Message Delivery Across Systems and Scales","authors":"W. Pepper Marts,&nbsp;Matthew G. F. Dosanjh,&nbsp;Whit Schonbein,&nbsp;Scott Levy,&nbsp;Patrick G. Bridges","doi":"10.1002/cpe.8342","DOIUrl":"https://doi.org/10.1002/cpe.8342","url":null,"abstract":"<div>\u0000 \u0000 <p>Early-bird communication is a communication/computation overlap technique that leverages fine-grained communication to improve application run-time. Communication is divided such that each individual thread can initiate transmission of its portion of the data upon completion rather than waiting for a dedicated communication phase. The benefit of early-bird communication depends on the completion timing of the individual threads: On the one hand, if all threads are complete at nearly the same time, the overheads of sending multiple messages will accumulate, leading to performance that is worse than if a single message had been sent. On the other hand, if thread completions are spread out in time, those that complete earlier can send data while others continue working, leading to performance that is better than if a single message had been sent. The challenge is that the completion times are currently unknown and can vary based on application, problem size, system software, and underlying hardware. In this paper, we address this lacuna by measuring and evaluating the potential overlap afforded by early-bird communication for a selection of proxy applications. These measurements help us understand whether a given application could benefit from early-bird communication. We present our technique for gathering this data and evaluate data collected from three proxy applications: MiniFE, MiniMD, and MiniQMC. Each application is run on three systems with distinct CPU architectures and strong scales across three run sizes. To characterize the behavior of these workloads, we study the trends of thread timings at both a macro level, across all threads across all runs of an application, and a micro level, that is, within a single process of a single run. We observe that our tested applications exhibit significantly different thread arrival distributions. The machine used had a significant impact, with the window of potential overlap varying by as much as an order of magnitude.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Workflow Scheduling Approach With Explicit Evolutionary Multi-Objective Multi-Task Optimization Algorithm in Cloud Environment","authors":"Qiqi Zhang,&nbsp;Bohui Li,&nbsp;Shaojin Geng,&nbsp;Xingjuan Cai","doi":"10.1002/cpe.8337","DOIUrl":"https://doi.org/10.1002/cpe.8337","url":null,"abstract":"<div>\u0000 \u0000 <p>Workflow tasks in the cloud environment are the abstraction and decomposition of large-scale and complex tasks in real-world scenarios, so cloud workflow scheduling problems have important research significance. However, most of the existing cloud workflow scheduling schemes are aimed at a single workflow, and do not make reasonable use of the commonality or complementary knowledge between similar tasks. Moreover, most cloud workflow scheduling models mainly focus on a few objectives such as time or cost, which is not comprehensive enough. Therefore, this paper first proposes a multi-objective cloud workflow scheduling model, which solves the maximum completion time, execution cost and energy consumption as three objectives during task execution. Secondly, to efficiently handle multiple similar cloud workflow scheduling tasks at the same time, this paper treats various cloud workflow scheduling issues as distinct tasks, establishes a multi-task cloud workflow scheduling framework that aims for the same goal while accommodating workflows of differing scales, and a multi-objective evolutionary multi-task optimization algorithm based on elite selection (MOEMT-ES) is designed to solve the above scheduling model. Finally, through algorithm comparison experiments on the CEC2017 evolutionary multi-task optimization competition benchmark problem and multi-workflow test problem, MOEMT-ES shows superior competitiveness.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis and Fuzzy Neural Networks-Based Inertia Coefficient Adjustment Strategy of Power Converters","authors":"Xing Dongfeng,&nbsp;Tian Mingxing","doi":"10.1002/cpe.8311","DOIUrl":"https://doi.org/10.1002/cpe.8311","url":null,"abstract":"<div>\u0000 \u0000 <p>With the development of new energy generation technology and power electronic technology, power electronic equipment occupies an increasing proportion of the power system, and its control flexibility makes the power system complicated, bringing problems such as low inertia, low damping, high harmonics, low reliability, and weak anti-interference ability. Especially the influence of converter on inertia of power system, even causing excessive frequency fluctuations, leading to the collapse of the power system. In view of the inertia problem of converters, this article starts with the principle of converters and analyzes the inertia transmission characteristics of different types of converters in power systems by three types of control strategies. Based on the influence of the inertia parameters of converters on system frequency and power, a converter inertia target function is established, and a neural network adaptive adjustment strategy for converter inertia coefficient is proposed to achieve self-adaptive optimization of converter output power and system frequency. The corresponding converter model is established, and the simulation circuit and control model are built by Simulink to verify the inertia transfer characteristics. The simulation results show the correctness of the relevant theories and provide theoretical support for the inertia design of high-proportion power electronic systems.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HRCUNet: Hierarchical Region Contrastive Learning for Segmentation of Breast Tumors in DCE-MRI","authors":"Jiezhou He,&nbsp;Zhiming Luo,&nbsp;Wei Peng,&nbsp;Songzhi Su,&nbsp;Xue Zhao,&nbsp;Guojun Zhang,&nbsp;Shaozi Li","doi":"10.1002/cpe.8319","DOIUrl":"https://doi.org/10.1002/cpe.8319","url":null,"abstract":"<div>\u0000 \u0000 <p>Segmenting breast tumors from dynamic contrast-enhanced magnetic resonance images is a critical step in the early detection and diagnosis of breast cancer. However, this task becomes significantly more challenging due to the diverse shapes and sizes of tumors, which make it difficult to establish a unified perception field for modeling them. Moreover, tumor regions are often subtle or imperceptible during early detection, exacerbating the issue of extreme class imbalance. This imbalance can lead to biased training and challenge accurately segmenting tumor regions from the predominant normal tissues. To address these issues, we propose a hierarchical region contrastive learning approach for breast tumor segmentation. Our approach introduces a novel hierarchical region contrastive learning loss function that addresses the class imbalance problem. This loss function encourages the model to create a clear separation between feature embeddings by maximizing the inter-class margin and minimizing the intra-class distance across different levels of the feature space. In addition, we design a novel Attention-based 3D Multi-scale Feature Fusion Residual Module to explore more granular multi-scale representations to improve the feature learning ability of tumors. Extensive experiments on two breast DCE-MRI datasets demonstrate that the proposed algorithm is more competitive against several state-of-the-art approaches under different segmentation metrics.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the effects of RNNs and deep learning frameworks on real-time, lightweight, adaptive time series anomaly detection","authors":"Ming-Chang Lee,&nbsp;Jia-Chun Lin,&nbsp;Sokratis Katsikas","doi":"10.1002/cpe.8288","DOIUrl":"https://doi.org/10.1002/cpe.8288","url":null,"abstract":"<p>Real-time, lightweight, adaptive time series anomaly detection is increasingly critical in cybersecurity, industrial control, finance, healthcare, and many other domains due to its capability to promptly process time series and detect anomalies without requiring extensive computation resources. While numerous anomaly detection approaches have emerged recently, they generally employ a single type of recurrent neural network (RNN) and are implemented using a single type of deep learning framework. The impacts of using various RNN types across different deep learning frameworks on the performance of these approaches remain unclear due to a lack of comprehensive evaluations. In this article, we aim to investigate the impact of different RNN variants and deep learning frameworks on real-time, lightweight, and adaptive time series anomaly detection. We reviewed several state-of-the-art anomaly detection approaches and implemented a representative approach using several RNN variants supported by three popular deep learning frameworks. A thorough evaluation was conducted to analyze the detection accuracy, time efficiency, and resource consumption of each implementation using four real-world, open-source time series datasets. The results show that RNN variants and deep learning frameworks have a significant impact. Therefore, it is crucial to carefully select appropriate RNN variants and deep learning frameworks for the implementation.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.8288","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey on Heterogeneous CPU–GPU Architectures and Simulators","authors":"Mohammad Alaei,&nbsp;Fahimeh Yazdanpanah","doi":"10.1002/cpe.8318","DOIUrl":"https://doi.org/10.1002/cpe.8318","url":null,"abstract":"<div>\u0000 \u0000 <p>Heterogeneous architectures are vastly used in various high performance computing systems from IoT-based embedded architectures to edge and cloud systems. Although heterogeneous architectures with cooperation of CPUs and GPUs and unified address space are increasingly used, there are still a lot of open questions and challenges regarding the design of these architectures. For evaluation, validation and exploration of next generation of heterogeneous CPU–GPU architectures, it is essential to use unified heterogeneous simulators for analyzing the execution of CPU–GPU workloads. This article presents a systematic review on challenges of heterogeneous CPU–GPU architectures with covering a diverse set of literatures on each challenge. The main considered challenges are shared resource management, network interconnections, task scheduling, energy consumption, and programming model. In addition, in this article, the state-of-the-art of heterogeneous CPU–GPU simulation platforms is reviewed. The structure and characteristics of five cycle-accurate heterogeneous CPU–GPU simulators are described and compared. We perform comprehensive discussions on the methodologies and challenges of designing high performance heterogeneous architectures. Moreover, for developing efficient heterogeneous CPU–GPU simulators, some recommendations are presented.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142869237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales","authors":"Xingfu Wu,&nbsp;Prasanna Balaprakash,&nbsp;Michael Kruse,&nbsp;Jaehoon Koo,&nbsp;Brice Videau,&nbsp;Paul Hovland,&nbsp;Valerie Taylor,&nbsp;Brad Geltz,&nbsp;Siddhartha Jana,&nbsp;Mary Hall","doi":"10.1002/cpe.8322","DOIUrl":"https://doi.org/10.1002/cpe.8322","url":null,"abstract":"<p>As we enter the exascale computing era, efficiently utilizing power and optimizing the performance of scientific applications under power and energy constraints has become critical and challenging. We propose a low-overhead autotuning framework to autotune performance and energy for various hybrid MPI/OpenMP scientific applications at large scales and to explore the tradeoffs between application runtime and power/energy for energy efficient application execution, then use this framework to autotune four ECP proxy applications—XSBench, AMG, SWFFT, and SW4lite. Our approach uses Bayesian optimization with a Random Forest surrogate model to effectively search parameter spaces with up to 6 million different configurations on two large-scale HPC production systems, Theta at Argonne National Laboratory and Summit at Oak Ridge National Laboratory. The experimental results show that our autotuning framework at large scales has low overhead and achieves good scalability. Using the proposed autotuning framework to identify the best configurations, we achieve up to 91.59% performance improvement, up to 21.2% energy savings, and up to 37.84% EDP (energy delay product) improvement on up to 4096 nodes.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.8322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142869239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Revocable Certificateless Encryption Scheme for Multi-User","authors":"Yiming Mou,&nbsp;Lunzhi Deng,&nbsp;Yu Ruan","doi":"10.1002/cpe.8321","DOIUrl":"https://doi.org/10.1002/cpe.8321","url":null,"abstract":"<div>\u0000 \u0000 <p>Certificateless cryptography, which solves the key escrow problem and avoids the complexity of certificate management, is an important part of public key cryptography. In the multi-user scenarios, broadcast encryption can improve computational efficiency and reduce communication cost. Moreover, there may be some malicious users in the above scenarios, and the decryption permissions of these users need an effective mechanism to revoke them. In this paper, a revocable certificateless encryption scheme for multi-user (RCLE-MU) is proposed to address this issue. In the scheme, the Cloud Server Provider (CSP) utilizes the master time key to periodically generate time keys for legitimate users. For illegitimate users, their decryption privileges are revoked since they are unable to obtain the time keys. Then this new scheme is proved to be ciphertext indistinguishable under selected identity and chosen-ciphertext attacks (sID-CCAs). Finally, compared with several other schemes, the new scheme has more efficiency advantage. So it is suitable for multi-user scenarios.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142869238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving QoS in cloud resources scheduling using dynamic clustering algorithm and SM-CDC scheduling model","authors":"Tayebeh Varmeziar,&nbsp;Mohamad Ebrahim Shiri,&nbsp;Parisa Rahmani","doi":"10.1002/cpe.8279","DOIUrl":"https://doi.org/10.1002/cpe.8279","url":null,"abstract":"<div>\u0000 \u0000 <p>Quality of Service (QoS) regulates and controls network resources by setting priorities for specific data types. Many clustering algorithms are used to cluster cloud workloads, most of which are static. However, the lack of dynamic algorithms is seen in the face of huge databases that are real-time and according to the existing clustering conditions. Additionally, fair allocation of tasks on servers and efficient resource utilization pose challenges. In this research, two solutions are proposed to improve the quality of service: the first solution uses the chameleon dynamic algorithm, a method to improve service quality. The chameleon algorithm has been able to show significant performance due to its high accuracy in detecting the smallest distance between clusters. This dynamic algorithm outperforms static algorithms with classification accuracy and response speed, which are the most important parameters of service quality. The second part of the proposed solution is to use the Scheduling Model using Cloud Data Centers (SM-CDC) system to select the best service provider based on the clustering done in the previous step. A SM-CDC technique is developed to handle cloud storage center tasks that are stored in electronic devices. According to the comparison with existing scheduling policies, SM-CDC offered 36% decrease on response time, 50% reduction on cost of resources, and 40% improvement on QoS Satisfaction.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 27","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harmonic Migration Algorithm for Virtual Machine Migration and Switching Strategy in Cloud Computing","authors":"Vahini Siruvoru,&nbsp;Shivampeta Aparna","doi":"10.1002/cpe.8320","DOIUrl":"https://doi.org/10.1002/cpe.8320","url":null,"abstract":"<div>\u0000 \u0000 <p>Nowadays, cloud computing (CC) has been utilized broadly owing to the services it provides which can be received from any location at any time on the basis of the customer's requirements. A huge amount of data transmission is made from both user to host as well as host to the customer in the cloud environment, but here placing the virtual machine (VM) on a suitable host and data transferring is a challenging task. In this research, a harmonic migration algorithm (HMA) is developed by combining both the migration algorithm (MA) and the harmonic analysis (HA) for migrating a VM from an overloaded to an under-loaded physical machine (PM) and enabling or disabling the VM through switching strategies in CC. The tasks are allocated to the corresponding VM in a round-robin (RR) manner and subsequently, the load of the VM is predicted through the gated recurrent unit (GRU). The HMA technique migrates the VM when the predicted load is higher than the value of threshold and also, it enables or disables the VM when necessary. Thus, the performance of the developed HMA is improved over the other previous schemes at tasks 100, 200, 300, and 400 by varying the iterations. Therefore, the predicted load, makespan, and resource utilization of the developed HMA are 0.148, 0.327 s, and 0.482% for task 100 at iteration 100.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142869103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}