Both energy and storage are becoming key issues in high-performance (HPC) systems, especially when thinking about upcoming Exascale systems. The amount of energy consumption and storage capacity needed to solve future problems is growing in a marked curve that the HPC community must face in cost-/energy-efficient ways. In this paper we provide a power-performance evaluation of HPC storage servers that take over tasks other than simply storing the data to disk. We use the Lustre parallel distributed file system with its ZFS back-end, which natively supports compression, to show that data compression can help to alleviate capacity and energy problems. In the first step of our analysis we study different compression algorithms with regards to their CPU and power overhead with a real dataset. Then, we use a modified version of the IOR benchmark to verify our claims for the HPC environment. The results demonstrate that the energy consumption can be reduced by up to 30% in the write phase of the application and 7% for write-intensive applications. At the same time, the required storage capacity can be reduced by approximately 50%. These savings can help in designing more power-efficient and leaner storage systems.

@inproceedings{EPBODCIHSS14,
	author	 = {Konstantinos Chasapis and Manuel Dolz and Michael Kuhn and Thomas Ludwig},
	title	 = {{Evaluating Power-Performace Benefits of Data Compression in HPC Storage Servers}},
	year	 = {2014},
	month	 = {04},
	booktitle	 = {{ENERGY 2014: The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies}},
	editor	 = {Steffen Fries and Petre Dini},
	publisher	 = {IARIA XPS Press},
	pages	 = {29--34},
	conference	 = {ENERGY 2014},
	location	 = {Chamonix, France},
	isbn	 = {978-1-61208-332-2},
	issn	 = {2308-412X},
	abstract	 = {Both energy and storage are becoming key issues in high-performance (HPC) systems, especially when thinking about upcoming Exascale systems. The amount of energy consumption and storage capacity needed to solve future problems is growing in a marked curve that the HPC community must face in cost-/energy-efficient ways. In this paper we provide a power-performance evaluation of HPC storage servers that take over tasks other than simply storing the data to disk. We use the Lustre parallel distributed file system with its ZFS back-end, which natively supports compression, to show that data compression can help to alleviate capacity and energy problems. In the first step of our analysis we study different compression algorithms with regards to their CPU and power overhead with a real dataset. Then, we use a modified version of the IOR benchmark to verify our claims for the HPC environment. The results demonstrate that the energy consumption can be reduced by up to 30\% in the write phase of the application and 7\% for write-intensive applications. At the same time, the required storage capacity can be reduced by approximately 50\%. These savings can help in designing more power-efficient and leaner storage systems.},
	url	 = {https://www.thinkmind.org/index.php?view=article&articleid=energy_2014_2_30_30061},
}