QuADD: QUantifying accelerator disaggregated datacenter efficiency

Guleria, A and Lakshmi, J and Padala, C (2019) QuADD: QUantifying accelerator disaggregated datacenter efficiency. In: 12th IEEE International Conference on Cloud Computing, CLOUD 2019, 8 July 2019- 13 July 2019, Milan, pp. 349-357.

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Abstract

In the current era of data explosion accelerators such as GPUs facilitate data-driven applications with requisite compute boost. Availability of GPUs in Public Cloud offerings has expedited their mass adoption. Consequently, varied customer demands and exclusive allocation of GPUs to VMs can leave stranded GPUs across the datacenter. Hardware disaggregation can alleviate this issue to enable a powerefficient datacenter. However, it is important to first quantify the gains associated with this new deployment paradigm. In the absence of real deployments, simulations can be helpful to evaluate the benefits of disaggregation at scale. In this paper, we evaluate the gains associated primarily with disaggregated GPU deployments. For this, we use QUADD-SIM, a simulator we built to model, quantify, and contrast different facets of these emerging GPU deployments. Using QUADD-SIM we model different VM and resource provisioning aspects of disaggregated GPU deployments. We simulate realistic AI workload requests for a period of 3 months with characteristics derived from recent public datacenter traces. Our results attest that disaggregated GPU deployment strategies outperform traditional GPU deployments in terms of failed VM requests and GPU Watt-hours consumption. Overall, 5.14 and 7.90 additional failed VM requests were serviced by disaggregated GPU deployments consuming 10.92 and 3.30 lesser GPU Watt-hours compared to traditional deployment. © 2019 IEEE.

Item Type:	Conference Paper
Publication:	IEEE International Conference on Cloud Computing, CLOUD
Publisher:	IEEE Computer Society
Additional Information:	The copyright for this article belongs to IEEE Computer Society
Keywords:	Cloud computing; Efficiency; Program processors; Simulators, Customer demands; Data explosion; Data-driven applications; Datacenter; Deployment strategy; Disaggregation; Power efficient; Public clouds, Computer hardware
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences Others
Date Deposited:	19 Dec 2022 07:32
Last Modified:	19 Dec 2022 07:32
URI:	https://eprints.iisc.ac.in/id/eprint/78508

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