Concurrency and Computation-Practice & Experience最新文献

{"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":"Auto-Tuning Mixed-Precision Computation by Specifying Multiple Regions","authors":"Xuanzhengbo Ren,&nbsp;Masatoshi Kawai,&nbsp;Tetsuya Hoshino,&nbsp;Takahiro Katagiri,&nbsp;Toru Nagai","doi":"10.1002/cpe.8326","DOIUrl":"https://doi.org/10.1002/cpe.8326","url":null,"abstract":"<div>\u0000 \u0000 <p>Mixed-precision computation is a promising method for substantially improving high-performance computing applications. However, using mixed-precision data is a double-edged sword. While it can improve computational performance, the reduction in precision introduces more uncertainties and errors. As a result, precision tuning is necessary to determine the optimal mixed-precision configurations. Much effort is therefore spent on selecting appropriate variables while balancing execution time and numerical accuracy. Auto-tuning (AT) is one of the technologies that can assist in alleviating this intensive task. In recent years, ppOpen-AT, an AT language, introduced a directive for mixed-precision tuning called “Blocks.” In this study, we investigated an AT strategy for the “Blocks” directive for multi-region tuning of a program. The non-hydrostatic icosahedral atmospheric model (NICAM), a global cloud-resolving model, was used as a benchmark program to evaluate the effectiveness of the AT strategy. Experimental results indicated that when a single region of the program performed well in mixed-precision computation, combining these regions resulted in better performance. When tested on the supercomputer “Flow” Type I (Fujitsu PRIMEHPC FX1000) and Type II (Fujitsu PRIMEHPC CX1000) subsystems, the mixed-precision NICAM benchmark program tuned by the AT strategy achieved a speedup of nearly 1.31× on the Type I subsystem compared to the original double-precision program, and a 1.12× speedup on the Type II subsystem.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112872","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":"Complete Visibility Algorithms of Luminous Robots With Two-Color Lights on Grid","authors":"Yonghwan Kim,&nbsp;Yoshiaki Katayama,&nbsp;Koichi Wada","doi":"10.1002/cpe.8323","DOIUrl":"https://doi.org/10.1002/cpe.8323","url":null,"abstract":"<div>\u0000 \u0000 <p>An autonomous mobile robot system is a distributed system consisting of multiple mobile computational entities, called robots, which autonomously and repeatedly perform three fundamental operations: look, compute, and move. Various challenges in such systems, including gathering, pattern formation, and flocking, have been extensively studied to explore the relationship between the robots' capabilities and the feasibility of solving these problems (i.e., solvability). In this study, we focus on the <i>complete visibility problem</i>, which aims to relocate all robots on an infinite grid plane so that every robot is visible to every other robot (i.e., <i>complete visibility</i>). We assume that each robot is a luminous robot (i.e., has a light with a constant number of colors) and opaque (non-transparent). This paper primarily examines the number of light colors required for each robot to solve the complete visibility problem. Specifically, we investigate the question: “<i>how many colors can we reduce while still achieving complete visibility?</i>” (even under certain stronger assumptions). As an answer to the above question, we show the existence of a deterministic algorithm to achieve complete visibility (i.e., every robot can observe all the other robots) using only two colors of light, if the robots agree on the directions and orientations of both axes. The proposed algorithm correctly works even if robots operate asynchronously and have no knowledge of the total number of robots. Moreover, its spatial complexity (i.e., the area of the smallest enclosed rectangle that includes all robots in the final configuration) ensures the optimal one, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>O</mi>\u0000 <mfenced>\u0000 <msup>\u0000 <mi>n</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mfenced>\u0000 </mrow>\u0000 <annotation>$$ mathcal{O}left({n}^2right) $$</annotation>\u0000 </semantics></math>, where <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 <annotation>$$ n $$</annotation>\u0000 </semantics></math> is the number of robots.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112215","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}

{"title":"Mapping Health Pathways: A Network Analysis for Improved Illness Prediction","authors":"Ankur Kumar Singhal,&nbsp;Anurag Singh","doi":"10.1002/cpe.8301","DOIUrl":"https://doi.org/10.1002/cpe.8301","url":null,"abstract":"<div>\u0000 \u0000 <p>Complex networks and network reconstruction have now become powerful tools for exploring relationships and interactions within systems across various fields to identify patterns and increase predictive accuracy. It provides a structured framework to investigate the relations or connections among individual components or entities within a system. Recently, the importance of the healthcare prediction model in life-saving efforts has increased. A model is proposed for health prediction that uses network reconstruction methods to build a network from available information, representing features as nodes and the relationships between them as edges. Subsequently, a method is introduced to calculate the value of the decision parameter (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 </mrow>\u0000 <annotation>$$ alpha $$</annotation>\u0000 </semantics></math>) for predicting an individual's health status. The proposed model shows substantial improvement over the current state of the prediction model. The first aim of the proposed model is to classify an individual into their appropriate class properly. Another contribution of the proposed model is to measure the factors that are responsible for classifying an individual into a class that shows its significance and impact over the existing state-of-the-art. It provides a new dimension to the prediction mode, emphasizing the importance of identifying critical features and their interdependencies for personalized health diagnostics.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 28","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737382","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}