TY - GEN
T1 - Survey on Memory and Devices Disaggregation Solutions for HPC Systems
AU - Bielski, MacIej
AU - Pinto, Christian
AU - Raho, Daniel
AU - Pacalet, Renaud
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/7/14
Y1 - 2017/7/14
N2 - Traditionally, HPC workloads are characterized by different requirements in CPU and memory resources, which in addition vary over time in unpredictable manner. For this reason, HPC system designs, assuming physical co-location of CPU and memory on a single motherboard, strongly limit scalability, while leading to inefficient resources over-provisioning. Also, peripherals available in the system need to be globally accessible to allow optimal usage. In this context, modern HPC designs tend to support disaggregated memory, compute nodes, remote peripherals and hardware extensions to support virtualization techniques. In this paper, a qualitative survey on different attempts of memory and devices disaggregation is conducted. In addition, alternative future directions for devices disaggregation are proposed in the context of the work planned in the H2020 dRedBox project.
AB - Traditionally, HPC workloads are characterized by different requirements in CPU and memory resources, which in addition vary over time in unpredictable manner. For this reason, HPC system designs, assuming physical co-location of CPU and memory on a single motherboard, strongly limit scalability, while leading to inefficient resources over-provisioning. Also, peripherals available in the system need to be globally accessible to allow optimal usage. In this context, modern HPC designs tend to support disaggregated memory, compute nodes, remote peripherals and hardware extensions to support virtualization techniques. In this paper, a qualitative survey on different attempts of memory and devices disaggregation is conducted. In addition, alternative future directions for devices disaggregation are proposed in the context of the work planned in the H2020 dRedBox project.
KW - HPC
KW - HPC virtualization
KW - cpu disaggregation
KW - memory disaggregation
U2 - 10.1109/CSE-EUC-DCABES.2016.185
DO - 10.1109/CSE-EUC-DCABES.2016.185
M3 - Conference contribution
AN - SCOPUS:85026654647
T3 - Proceedings - 19th IEEE International Conference on Computational Science and Engineering, 14th IEEE International Conference on Embedded and Ubiquitous Computing and 15th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, CSE-EUC-DCABES 2016
SP - 197
EP - 204
BT - Proceedings - 19th IEEE International Conference on Computational Science and Engineering, 14th IEEE International Conference on Embedded and Ubiquitous Computing and 15th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, CSE-EUC-DCABES 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Computational Science and Engineering, 14th IEEE International Conference on Embedded and Ubiquitous Computing and 15th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, CSE-EUC-DCABES 2016
Y2 - 24 August 2016 through 26 August 2016
ER -