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Naweiluo Zhou

ORCID iD icon https://orcid.org/0000-0001-9329-4500

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Publications

2023

N. Zhou, F. Dufour, V. Bode, P. Zinterhof, N. J. Hammer, and D. Kranzlmüller, “Towards confidential computing: A secure cloud architecture for big data analytics and AI,” in 2023 IEEE 16th International Conference on Cloud Computing (CLOUD), IEEE, 2023.

N. Zhou, H. Zhou and D. Hoppe, "Containerization for High Performance Computing Systems: Survey and Prospects" in IEEE Transactions on Software Engineering, vol. 49, no. 04, pp. 2722-2740, 2023. doi: 10.1109/TSE.2022.3229221

2022

B. Pejak, P. Lugonja, A. Anti, M. Pani, M. Pandi, E. Alexakis, P. Mavrepis, N. Zhou, O. Marko, and V. Crnojevi, “Soya yield prediction on a within-field scale using machine learning models trained on sentinel-2 and soil data,” Remote Sensing, vol. 14, no. 9, 2022.

N. Zhou, L. Zhong, D. Hoppe, B. Pejak, O. Marko, J. Cardona, M. Czerkawski, I. Andonovic, C. Michie, C. Tachtatzis, et al., “Cybele: A hybrid architecture of hpc and big data for ai applications in agriculture,” in HPC, Big Data, and AI Convergence Towards Exascale, pp. 255–272, CRC Press, 2022.

2021

N. Zhou, “Containerization and orchestration on hpc systems,” in Sustained Simulation Performance 2019 and 2020: Proceedings of the Joint Workshop on Sustained Simulation Performance, University of Stuttgart (HLRS) and Tohoku University, 2019 and 2020, pp. 133–147, Springer, 2021.

T. Chen, L. Zhong, N. Zhou, and D. Hoppe, “Catch weight prediction for multi-species fishing using artificial neural networks,” in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1545–1552, IEEE, 2021.

L. Zhong, D. Hoppe, N. Zhou, and O. Shcherbakov, “Hybrid workflow of simulation and deep learning on hpc: A case study for material behavior determination,” in 2021 IEEE International Conference on Cluster Computing (CLUSTER), pp. 698–704, IEEE, 2021.

N. Zhou, Y. Georgiou, M. Pospieszny, L. Zhong, H. Zhou, C. Niethammer, B. Pejak, O. Marko, and D. Hoppe, “Container orchestration on hpc systems through kubernetes,” Journal of Cloud Computing, vol. 10, no. 1, pp. 1–14, 2021.

2020

H. Zhou, J. Gracia, N. Zhou, and R. Schneider, “Collectives in hybrid mpi+ mpi code: Design, practice and performance,” Parallel Computing, vol. 99, p. 102669, 2020.

N. Zhou, Y. Georgiou, L. Zhong, H. Zhou, and M. Pospieszny, “Container orchestration on hpc systems,” in 2020 IEEE 13th International Conference on Cloud Computing (CLOUD), pp. 34–36, IEEE, 2020.

Y. Georgiou, N. Zhou, L. Zhong, D. Hoppe, M. Pospieszny, N. Papadopoulou, K. Nikas, O. L. Nikolos, P. Kranas, S. Karagiorgou, et al., “Converging hpc, big data and cloud technologies for precision agriculture data analytics on supercomputers,” in High Performance Computing: ISC High Performance 2020 International Workshops, Frankfurt, Germany, June 21–25, 2020, Revised Selected Papers 35, pp. 368–379, Springer, 2020.

2018

N. Zhou, G. Delaval, B. Robu, É. Rutten, and J.-F. Méhaut, “An autonomic-computing approach on mapping threads to multi-cores for software transactional memory,” Concurrency and Computation: Practice and Experience, vol. 30, no. 18, p. e4506, 2018.

2016

N. Zhou, Autonomic Thread Parallelism and Mapping Control for Software Transactional Memory. PhD thesis, Université Grenoble Alpes (ComUE), 2016.

N. Zhou, G. Delaval, B. Robu, E. Rutten, and J.-F. Méhaut, “Autonomic parallelism and thread mapping control on software transactional memory,” in 2016 IEEE International Conference on Autonomic Computing (ICAC), pp. 189–198, IEEE, 2016.

N. Zhou, G. Delaval, B. Robu, E. Rutten, and J.-F. Méhaut, “Control of autonomic parallelism adaptation on software transactional memory,” in 2016 International Conference on High Performance Computing & Simulation (HPCS), pp. 180–187, IEEE, 2016.

N. Zhou, G. Delaval, B. Robu, É. Rutten, and J.-F. Méhaut, “Autonomic Parallelism Adaptation for Software Transactional Memory,” in ComPAS 2016 - Conférence francophone d’informatique en parallélisme, architecture et système, (Lorient, France), jul 2016.

N. Zhou, G. Delaval, B. Robu, É. Rutten, and J.-F. Méhaut, “Autonomic parallelism adaptation on software transactional memory,” tech. rep., 2016.

Kontakt

Email: firstname.lastname@ieee.org
Dieser Server wird am Leibniz-Rechenzentrum der Bayerischen Akademie der Wissenschaften gehostet. Sie können die LRZ-Webmaster über den Servicedesk kontaktieren.

Naweiluo Zhou

ORCID iD icon https://orcid.org/0000-0001-9329-4500

Naweiluo Zhou is a research scientist in Leibniz Supercomputing Centre (LRZ).

Publications

2023

N. Zhou, F. Dufour, V. Bode, P. Zinterhof, N. J. Hammer, and D. Kranzlmüller, “Towards confidential computing: A secure cloud architecture for big data analytics and AI,” in 2023 IEEE 16th International Conference on Cloud Computing (CLOUD), IEEE, 2023.

N. Zhou, H. Zhou and D. Hoppe, "Containerization for High Performance Computing Systems: Survey and Prospects" in IEEE Transactions on Software Engineering, vol. 49, no. 04, pp. 2722-2740, 2023. doi: 10.1109/TSE.2022.3229221

2022

B. Pejak, P. Lugonja, A. Anti, M. Pani, M. Pandi, E. Alexakis, P. Mavrepis, N. Zhou, O. Marko, and V. Crnojevi, “Soya yield prediction on a within-field scale using machine learning models trained on sentinel-2 and soil data,” Remote Sensing, vol. 14, no. 9, 2022.

N. Zhou, L. Zhong, D. Hoppe, B. Pejak, O. Marko, J. Cardona, M. Czerkawski, I. Andonovic, C. Michie, C. Tachtatzis, et al., “Cybele: A hybrid architecture of hpc and big data for ai applications in agriculture,” in HPC, Big Data, and AI Convergence Towards Exascale, pp. 255–272, CRC Press, 2022.

2021

N. Zhou, “Containerization and orchestration on hpc systems,” in Sustained Simulation Performance 2019 and 2020: Proceedings of the Joint Workshop on Sustained Simulation Performance, University of Stuttgart (HLRS) and Tohoku University, 2019 and 2020, pp. 133–147, Springer, 2021.

T. Chen, L. Zhong, N. Zhou, and D. Hoppe, “Catch weight prediction for multi-species fishing using artificial neural networks,” in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1545–1552, IEEE, 2021.

L. Zhong, D. Hoppe, N. Zhou, and O. Shcherbakov, “Hybrid workflow of simulation and deep learning on hpc: A case study for material behavior determination,” in 2021 IEEE International Conference on Cluster Computing (CLUSTER), pp. 698–704, IEEE, 2021.

N. Zhou, Y. Georgiou, M. Pospieszny, L. Zhong, H. Zhou, C. Niethammer, B. Pejak, O. Marko, and D. Hoppe, “Container orchestration on hpc systems through kubernetes,” Journal of Cloud Computing, vol. 10, no. 1, pp. 1–14, 2021.

2020

H. Zhou, J. Gracia, N. Zhou, and R. Schneider, “Collectives in hybrid mpi+ mpi code: Design, practice and performance,” Parallel Computing, vol. 99, p. 102669, 2020.

N. Zhou, Y. Georgiou, L. Zhong, H. Zhou, and M. Pospieszny, “Container orchestration on hpc systems,” in 2020 IEEE 13th International Conference on Cloud Computing (CLOUD), pp. 34–36, IEEE, 2020.

Y. Georgiou, N. Zhou, L. Zhong, D. Hoppe, M. Pospieszny, N. Papadopoulou, K. Nikas, O. L. Nikolos, P. Kranas, S. Karagiorgou, et al., “Converging hpc, big data and cloud technologies for precision agriculture data analytics on supercomputers,” in High Performance Computing: ISC High Performance 2020 International Workshops, Frankfurt, Germany, June 21–25, 2020, Revised Selected Papers 35, pp. 368–379, Springer, 2020.

2018

N. Zhou, G. Delaval, B. Robu, É. Rutten, and J.-F. Méhaut, “An autonomic-computing approach on mapping threads to multi-cores for software transactional memory,” Concurrency and Computation: Practice and Experience, vol. 30, no. 18, p. e4506, 2018.

2016

N. Zhou, Autonomic Thread Parallelism and Mapping Control for Software Transactional Memory. PhD thesis, Université Grenoble Alpes (ComUE), 2016.

N. Zhou, G. Delaval, B. Robu, E. Rutten, and J.-F. Méhaut, “Autonomic parallelism and thread mapping control on software transactional memory,” in 2016 IEEE International Conference on Autonomic Computing (ICAC), pp. 189–198, IEEE, 2016.

N. Zhou, G. Delaval, B. Robu, E. Rutten, and J.-F. Méhaut, “Control of autonomic parallelism adaptation on software transactional memory,” in 2016 International Conference on High Performance Computing & Simulation (HPCS), pp. 180–187, IEEE, 2016.

N. Zhou, G. Delaval, B. Robu, É. Rutten, and J.-F. Méhaut, “Autonomic Parallelism Adaptation for Software Transactional Memory,” in ComPAS 2016 - Conférence francophone d’informatique en parallélisme, architecture et système, (Lorient, France), jul 2016.

N. Zhou, G. Delaval, B. Robu, É. Rutten, and J.-F. Méhaut, “Autonomic parallelism adaptation on software transactional memory,” tech. rep., 2016.

Contact

Email: firstname.lastname@ieee.org
Dieser Server wird am Leibniz-Rechenzentrum der Bayerischen Akademie der Wissenschaften gehostet. Sie können die LRZ-Webmaster über den Servicedesk kontaktieren.