Relevant bibliographies by topics / Compute-Intensive Applications

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Compute-Intensive Applications'

Author: Grafiati
Published: 3 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Compute-Intensive Applications"

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Houstis, Catherine, Sarantos Kapidakis, Evangelos P. Markatos, and Erol Gelenbe. "Execution of compute-intensive applications into parallel machines." Information Sciences 97, no. 1-2 (1997): 83–124. http://dx.doi.org/10.1016/s0020-0255(96)00174-0.

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Wang, Dong, Peng Cao, and Yang Xiao. "A parallel arithmetic array for accelerating compute-intensive applications." IEICE Electronics Express 11, no. 4 (2014): 20130981. http://dx.doi.org/10.1587/elex.11.20130981.

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Song, Xiaojia, Tao Xie, and Stephen Fischer. "Two Reconfigurable NDP Servers: Understanding the Impact of Near-Data Processing on Data Center Applications." ACM Transactions on Storage 17, no. 4 (2021): 1–27. http://dx.doi.org/10.1145/3460201.

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Existing near-data processing (NDP)-powered architectures have demonstrated their strength for some data-intensive applications. Data center servers, however, have to serve not only data-intensive but also compute-intensive applications. An in-depth understanding of the impact of NDP on various data center applications is still needed. For example, can a compute-intensive application also benefit from NDP? In addition, current NDP techniques focus on maximizing the data processing rate by always utilizing all computing resources at all times. Is this “always running in full gear” strategy cons
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Taifi, Moussa, Abdallah Khreishah, and Justin Y. Shi. "Building a Private HPC Cloud for Compute and Data-Intensive Applications." International Journal on Cloud Computing: Services and Architecture 3, no. 2 (2013): 1–20. http://dx.doi.org/10.5121/ijccsa.2013.3201.

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Shalini Lakshmi, A. J., and M. Vijayalakshmi. "A predictive context aware collaborative offloading framework for compute-intensive applications." Journal of Intelligent & Fuzzy Systems 40, no. 1 (2021): 77–88. http://dx.doi.org/10.3233/jifs-182906.

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The resourceful mobile devices with augmented capabilities around human pave the way for utilizing it as delegators for resource-constrained devices to run compute-intensive applications. Such collaborative resource sharing policy among mobile devices throws challenges like identifying competent alternatives for offloading and diminishing time consumption of pre-offload process to accomplish remarkable offloading. This paper presents a Mobile Cloud Computing framework with Predictive Context-Aware Collaborative Offloading Process (PCA-COP) that fixes these challenges through conductive alterna
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Moussa, Taifi1 Abdallah Khreishah2 and Justin Y. Shi1. "BUILDING A PRIVATE HPC CLOUD FOR COMPUTE AND DATA-INTENSIVE APPLICATIONS." International Journal on Cloud Computing: Services and Architecture (IJCCSA) 3, April (2018): 01–20. https://doi.org/10.5281/zenodo.1434573.

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Traditional HPC (High Performance Computing) clusters are best suited for well-formed calculations. The orderly batch-oriented HPC cluster offers maximal potential for performance per application, but limits resource efficiency and user flexibility. An HPC cloud can host multiple virtual HPC clusters, giving the scientists unprecedented flexibility for research and development. With the proper incentive model, resource efficiency will be automatically maximized. In this context, there are three new challenges. The first is the virtualization overheads. The second is the administrative complexi
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Ahuja, Sanjay P., and Bhagavathi Kaza. "Performance Evaluation of Data Intensive Computing In the Cloud." International Journal of Cloud Applications and Computing 4, no. 2 (2014): 34–47. http://dx.doi.org/10.4018/ijcac.2014040103.

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Big data is a topic of active research in the cloud community. With increasing demand for data storage in the cloud, study of data-intensive applications is becoming a primary focus. Data-intensive applications involve high CPU usage for processing large volumes of data on the scale of terabytes or petabytes. While some research exists for the performance effect of data intensive applications in the cloud, none of the research compares the Amazon Elastic Compute Cloud (Amazon EC2) and Google Compute Engine (GCE) clouds using multiple benchmarks. This study performs extensive research on the Am
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Nguyen, Giang, Viera Šipková, Stefan Dlugolinsky, Binh Minh Nguyen, Viet Tran, and Ladislav Hluchý. "A comparative study of operational engineering for environmental and compute-intensive applications." Array 12 (December 2021): 100096. http://dx.doi.org/10.1016/j.array.2021.100096.

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Aulov, V., K. De, D. Drizhuk, et al. "Workload Management Portal for High Energy Physics Applications and Compute Intensive Science." Procedia Computer Science 66 (2015): 564–73. http://dx.doi.org/10.1016/j.procs.2015.11.064.

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Ciżnicki, Miłosz, Michał Kierzynka, Piotr Kopta, Krzysztof Kurowski, and Paweł Gepner. "Benchmarking Data and Compute Intensive Applications on Modern CPU and GPU Architectures." Procedia Computer Science 9 (2012): 1900–1909. http://dx.doi.org/10.1016/j.procs.2012.04.208.

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Dissertations / Theses on the topic "Compute-Intensive Applications"

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Huije, Shen, and Tingwei Huang. "Spark for HPC: a comparison with MPI on compute-intensive applications using Monte Carlo method." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392311.

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With the emergence of various big data platforms in recent years, Apache Spark - a distributed large-scale computing platform, is perceived as a potential substitute for Message Passing Interface (MPI) in High Performance Computing (HPC). Due to the limitations in fault-tolerance, dynamic resource handling and ease of use, MPI, as a dominant method to achieve parallel computing in HPC, is often associated with higher development time and costs in enterprises such as Scania IT. This thesis project aims to examine Apache Spark as an alternative to MPI on HPC clusters and compare their performanc
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Engel, Andreas [Verfasser], Andreas [Akademischer Betreuer] Koch, and Christian [Akademischer Betreuer] Hochberger. "A Heterogeneous System Architecture for Low-Power Wireless Sensor Nodes in Compute-Intensive Distributed Applications / Andreas Engel ; Andreas Koch, Christian Hochberger." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2016. http://d-nb.info/1120585090/34.

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Engel, Andreas. "A Heterogeneous System Architecture for Low-Power Wireless Sensor Nodes in Compute-Intensive Distributed Applications." Phd thesis, 2016. https://tuprints.ulb.tu-darmstadt.de/5778/1/thesis.pdf.

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Wireless Sensor Networks (WSNs) combine embedded sensing and processing capabilities with a wireless communication infrastructure, thus supporting distributed monitoring applications. WSNs have been investigated for more than three decades, and recent social and industrial developments such as home automation, or the Internet of Things, have increased the commercial relevance of this key technology. The communication bandwidth of the sensor nodes is limited by the transportation media and the restricted energy budget of the nodes. To still keep up with the ever increasing sensor count and samp
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Book chapters on the topic "Compute-Intensive Applications"

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Kaushik, Rini T., and Klara Nahrstedt. "Energy-Conservation in Large-Scale Data-Intensive Hadoop Compute Clusters." In Green IT: Technologies and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22179-8_13.

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Dhar, Ashutosh, Paul Reckamp, Jinjun Xiong, Wen-mei Hwu, and Deming Chen. "Graviton: A Reconfigurable Memory-Compute Fabric for Data Intensive Applications." In Applied Reconfigurable Computing. Architectures, Tools, and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79025-7_18.

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Degerlund, Fredrik. "Scheduling of Compute-Intensive Code Generated from Event-B Models: An Empirical Efficiency Study." In Distributed Applications and Interoperable Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30823-9_15.

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Hachinger, Stephan, Martin Golasowski, Jan Martinovič, et al. "Leveraging High-Performance Computing and Cloud Computing with Unified Big-Data Workflows: The LEXIS Project." In Technologies and Applications for Big Data Value. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78307-5_8.

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AbstractTraditional usage models of Supercomputing centres have been extended by High-Throughput Computing (HTC), High-Performance Data Analytics (HPDA) and Cloud Computing. The complexity of current compute platforms calls for solutions to simplify usage and conveniently orchestrate computing tasks. These enable also non-expert users to efficiently execute Big Data workflows. In this context, the LEXIS project (‘Large-scale EXecution for Industry and Society’, H2020 GA 825532, https://lexis-project.eu) sets up an orchestration platform for compute- and data-intensive workflows. Its main objec
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Martínez, D. R., J. L. Albín, J. C. Cabaleiro, T. F. Pena, and F. F. Rivera. "A Load Balance Methodology for Highly Compute-Intensive Applications on Grids Based on Computational Modeling." In On the Move to Meaningful Internet Systems 2005: OTM 2005 Workshops. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11575863_52.

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Steinert, Fritjof, and Benno Stabernack. "FPGA-Based Network-Attached Accelerators – An Environmental Life Cycle Perspective." In Architecture of Computing Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42785-5_17.

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AbstractHomogeneous computing systems are reaching their limits with the growing demands of current applications. Accelerating compute-intensive applications ensures manageable computing times and boosts energy efficiency, which is an important lever as part of ongoing efforts to tackle global climate change. Field Programmable Gate Array (FPGA) accelerators are well-known for increasing throughput and, in particular, energy efficiency for many applications. FPGA accelerators connected directly to the data center high-speed network are ideal for integration into a heterogeneous data center, av
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Leyendecker, Lars, Shobhit Agarwal, Thorben Werner, Maximilian Motz, and Robert H. Schmitt. "A Study on Data Augmentation Techniques for Visual Defect Detection in Manufacturing." In Bildverarbeitung in der Automation. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66769-9_6.

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AbstractDeep learning-based defect detection is rapidly gaining importance for automating visual quality control tasks in industrial applications. However, due to usually low rejection rates in manufacturing processes, industrial defect detection datasets are inherent to three severe data challenges: data sparsity, data imbalance, and data shift. Because the acquisition of defect data is highly cost″​=intensive, and Deep Learning (DL) algorithms require a sufficiently large amount of data, we are investigating how to solve these challenges using data oversampling and data augmentation (DA) tec
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Suciu, Constantin, Lucian Itu, Cosmin Nita, et al. "GPU-Based High Performance Computing: Employing Massively Parallel Processors for Speeding-Up Compute Intensive Algorithms." In Patient-specific Hemodynamic Computations: Application to Personalized Diagnosis of Cardiovascular Pathologies. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56853-9_7.

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Truong, Dung, Kay Robbins, Arnaud Delorme, and Scott Makeig. "End-to-End Processing of M/EEG Data with BIDS, HED, and EEGLAB." In Neuromethods. Springer US, 2024. https://doi.org/10.1007/978-1-0716-4260-3_6.

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AbstractReliable and reproducible machine-learning enabled neuroscience research requires large-scale data sharing and analysis. Essential for the effective and efficient analysis of shared datasets are standardized data and metadata organization and formatting, a well-documented, automated analysis pipeline, a comprehensive software framework, and a compute environment that can adequately support the analysis process. In this chapter, we introduce the combined Brain Imaging Data Structure (BIDS) and Hierarchical Event Descriptors (HED) frameworks and illustrate their example use through the o
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Ahuja, Sanjay P., and Bhagavathi Kaza. "Performance Evaluation of Data Intensive Computing In the Cloud." In Cloud Technology. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6539-2.ch088.

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Big data is a topic of active research in the cloud community. With increasing demand for data storage in the cloud, study of data-intensive applications is becoming a primary focus. Data-intensive applications involve high CPU usage for processing large volumes of data on the scale of terabytes or petabytes. While some research exists for the performance effect of data intensive applications in the cloud, none of the research compares the Amazon Elastic Compute Cloud (Amazon EC2) and Google Compute Engine (GCE) clouds using multiple benchmarks. This study performs extensive research on the Am
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Conference papers on the topic "Compute-Intensive Applications"

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Nagiyev, Andrey, Enes Bajrovic, and Siegfried Benkner. "Python to Kubernetes: A Programming and Resource Management Framework for Compute-and Data-intensive Applications." In 2024 IEEE 30th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2024. https://doi.org/10.1109/icpads63350.2024.00069.

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Alhassoun, Nailah, and Nalini Venkatasubramanian. "Scaling Compute-Intensive Telehealth Monitoring in IoT-Enabled Smart Spaces: A Personal-Space-State Approach." In 2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom). IEEE, 2024. https://doi.org/10.1109/healthcom60970.2024.10880747.

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Rethinagiri, Santhosh Kumar, Oscar Palomar, Javier Arias Moreno, Osman Unsal, and Adrian Cristal. "Heterogeneous Platform to Accelerate Compute Intensive Applications." In 2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, 2015. http://dx.doi.org/10.1109/fccm.2015.62.

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Bhavsar, Madhuri, Jimesh R. Prajapati, and Archana Bhavsar. "Semantic enabled cloud for compute — Intensive applications." In 2016 International Conference on Internet of Things and Applications (IOTA). IEEE, 2016. http://dx.doi.org/10.1109/iota.2016.7562687.

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Jararweh, Yaser, Moath Jarrah, and Salim Hariri. "Exploiting GPUs for compute-intensive medical applications." In 2012 International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2012. http://dx.doi.org/10.1109/icmcs.2012.6320262.

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Gilani, Syed Zohaib, Nam Sung Kim, and Michael J. Schulte. "Power-efficient computing for compute-intensive GPGPU applications." In the 21st international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2370816.2370888.

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Che, Shuai, Jie Li, Jeremy W. Sheaffer, Kevin Skadron, and John Lach. "Accelerating Compute-Intensive Applications with GPUs and FPGAs." In 2008 Symposium on Application Specific Processors (SASP '08). IEEE, 2008. http://dx.doi.org/10.1109/sasp.2008.4570793.

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Gilani, S. Z., Nam Sung Kim, and M. J. Schulte. "Power-efficient computing for compute-intensive GPGPU applications." In 2013 IEEE 19th International Symposium on High Performance Computer Architecture (HPCA). IEEE, 2013. http://dx.doi.org/10.1109/hpca.2013.6522330.

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Huang, Tsung-Wei, Chun-Xun Lin, and Martin D. F. Wong. "DtCraft: A distributed execution engine for compute-intensive applications." In 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 2017. http://dx.doi.org/10.1109/iccad.2017.8203853.

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Noreikis, Marius, Yu Xiao, and Yuming Jiang. "Edge Capacity Planning for Real Time Compute-Intensive Applications." In 2019 IEEE International Conference on Fog Computing (ICFC). IEEE, 2019. http://dx.doi.org/10.1109/icfc.2019.00029.

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Journal articles
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