GPU virtualization and scheduling methods: A comprehensive survey

CH Hong, I Spence, DS Nikolopoulos - ACM Computing Surveys (CSUR …, 2017 - dl.acm.org
The integration of graphics processing units (GPUs) on high-end compute nodes has
established a new accelerator-based heterogeneous computing model, which now …

[HTML][HTML] Estimation of energy consumption in machine learning

E García-Martín, CF Rodrigues, G Riley… - Journal of Parallel and …, 2019 - Elsevier
Energy consumption has been widely studied in the computer architecture field for decades.
While the adoption of energy as a metric in machine learning is emerging, the majority of …

Characterization and prediction of deep learning workloads in large-scale gpu datacenters

Q Hu, P Sun, S Yan, Y Wen, T Zhang - Proceedings of the International …, 2021 - dl.acm.org
Modern GPU datacenters are critical for delivering Deep Learning (DL) models and services
in both the research community and industry. When operating a datacenter, optimization of …

A survey on run-time power monitors at the edge

D Zoni, A Galimberti, W Fornaciari - ACM Computing Surveys, 2023 - dl.acm.org
Effectively managing energy and power consumption is crucial to the success of the design
of any computing system, hel** mitigate the efficiency obstacles given by the downsizing …

The impact of GPU DVFS on the energy and performance of deep learning: An empirical study

Z Tang, Y Wang, Q Wang, X Chu - Proceedings of the Tenth ACM …, 2019 - dl.acm.org
Over the past years, great progress has been made in improving the computing power of
general-purpose graphics processing units (GPGPUs), which facilitates the prosperity of …

{EnvPipe}: Performance-preserving {DNN} training framework for saving energy

S Choi, I Koo, J Ahn, M Jeon, Y Kwon - 2023 USENIX Annual Technical …, 2023 - usenix.org
Energy saving is a crucial mission for data center providers. Among many services, DNN
training and inference are significant contributors to energy consumption. This work focuses …

Deep learning and machine learning with gpgpu and cuda: Unlocking the power of parallel computing

M Li, Z Bi, T Wang, Y Wen, Q Niu, J Liu, B Peng… - arxiv preprint arxiv …, 2024 - arxiv.org
This book presents a comprehensive exploration of GPGPU (General Purpose Graphics
Processing Unit) and its applications in deep learning and machine learning. It focuses on …

GPGPU performance estimation with core and memory frequency scaling

Q Wang, X Chu - IEEE Transactions on Parallel and Distributed …, 2020 - ieeexplore.ieee.org
Contemporary graphics processing units (GPUs) support dynamic voltage and frequency
scaling to balance computational performance and energy consumption. However, accurate …

Security in hardware assisted virtualization for cloud computing—State of the art issues and challenges

B Asvija, R Eswari, MB Bijoy - Computer Networks, 2019 - Elsevier
The advantages of virtualization technology have resulted in its wide spread adoption in
cloud computing infrastructures. However it has also introduced a new set of security threats …

Verified instruction-level energy consumption measurement for nvidia gpus

Y Arafa, A ElWazir, A ElKanishy, Y Aly… - Proceedings of the 17th …, 2020 - dl.acm.org
GPUs are prevalent in modern computing systems at all scales. They consume a significant
fraction of the energy in these systems. However, vendors do not publish the actual cost of …