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Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (1): 110-122.doi: 10.1007/s11390-020-0776-8
Special Issue: Computer Architecture and Systems
• Special Section on Memory-Centric System Research for High-Performance Computing • Previous Articles Next Articles
Michèle Weiland1 and Bernhard Homölle2
[1] Jackson A, Weiland M, Parsons M, Homölle B. An architecture for high performance computing and data systems using byte-addressable persistent memory. In Proc. the 2019 ISC High Performance International Workshops, June 2019, pp.258-274. DOI:10.1007/978-3-030-34356-921. [2] Izraelevitz J, Yang J, Zhang L et al. Basic performance measurements of the Intel Optane DC persistent memory module. arXiv:1903.05714, 2019. http://arxiv.org/abs/1903.05714, March 2020. [3] Patil O, Ionkov L, Lee J et al. Performance characterization of a DRAM-NVM hybrid memory architecture for HPC applications using Intel Optane DC persistent memory modules. In Proc. the International Symposium on Memory Systems, September 2019, pp.288-303. DOI:10.1145/3357526.3357541. [4] Mason T, Doudali T D, Seltzer M, Gavrilovska A. Unexpected performance of Intelr OptaneTM DC persistent memory. IEEE Computer Architecture Letters, 2020, 19(1):55-58. DOI:10.1109/LCA.2020.2987303. [5] Clark S J, Segall M D, Pickard C J et al. First principles methods using CASTEP. Zeitschrift für Kristallographie, 2005, 220:567-570. DOI:10.1524/zkri.220.5.567.65075. [6] Weiland M, Brunst H, Quintino T et al. An early evaluation of Intel's Optane DC persistent memory module and its impact on high-performance scientific applications. In Proc. the International Conference for High Performance Computing, Networking, Storage and Analysis, Nov. 2019, Article No. 76. DOI:10.1145/3295500.3356159. [7] Vef M A, Moti N, Süß T et al. GekkoFS|A temporary burst buffer file system for HPC applications. J. Comput. Sci. Technol., 2020, 35(1):72-91. DOI:10.1007/s11390-020- 9797-6. [8] Brinkmann A, Mohror K, Yu W et al. Ad hoc file systems for high-performance computing. J. Comput. Sci. Technol., 2020, 35(1):4-26. DOI:10.1007/s11390-020-9801-1. [9] Smart S, Quintino T, Raoult B. A scalable object store for meteorological and climate data. In Proc. the Platform for Advanced Scientific Computing Conference, June 2017, Article No. 13. DOI:10.1145/3093172.3093238. [10] Smart S, Quintino T, Raoult B. A high-performance distributed object-store for exascale numerical weather prediction and climate. In Proc. the Platform for Advanced Scientific Computing Conference, June 2019, Article No. 16. DOI:10.1145/3324989.3325726. [11] Weiland M, Jackson A, Johnson N, Parsons M. Exploiting the performance benefits of storage class memory for HPC and HPDA workflows. Journal of Supercomputing Frontiers and Innovations, 2018, 5(1):79-94. DOI:10.14529/jsfi180105. [12] Miranda A, Jackson A, Tocci T, Panourgias I, Nou R. NORNS:Extending slurm to support data-driven workflows through asynchronous data staging. In Proc. the 2019 IEEE International Conference on Cluster Computing, Sept. 2019. DOI:10.1109/CLUSTER.2019.8891014. [13] Brown N, Weiland M, Hill A et al. A highly scalable Met Office NERC Cloud model. In Proc. the 3rd International Conference on Exascale Applications and Software, April 2015, pp.132-137. DOI:10.5555/2820083.2820108. |
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[3] | Yuan-Chao Xu, Hu Wan, Ke-Ni Qiu, Tao Li, Wei-Gong Zhang. Reducing Synchronization Cost for Single-Level Store in Mobile Systems [J]. , 2016, 31(4): 836-848. |
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