DVFS_PaperList

Paper	Keywords	Institute (first)	Publication	Others
A survey and measurement study of GPU DVFS on energy conservation	DVFS survey	HKBU	Digital Communications and Networks
Characterizing Power Management Opportunities for LLMs in the Cloud	LLM	Microsoft Azure	ASPLOS 2024
Predict; Don’t React for Enabling Efficient Fine-Grain DVFS in GPUs	Fine Grain DVFS	MicroSoft	ASPLOS 2024
Roofline-aware DVFS for GPUs	roofline DVFS	Eindhoven University of Technology	ADAPT 2014
Effects of Dynamic Voltage and Frequency Scaling on a K20 GPU		Marquette University	ICPP 2013	Key Points: 1. MM benchmark shows performance is linearly proportional to the GPU frequency when using a high memory speed 2. CPU DVFS impacts the CPU and other system components
Evaluating the energy impact of device parameters for DNN inference on edge	DNN inference； edge device	Stony Brook University	IGSC 2023	Key Points: default parameter setting is not energy optimal; Power consumption increases linearly with frequency. The decrease in inference latency starts to plateau out at higher frequency as frequency is not the bottkeneck and performance is limited by other components such as memory and I/O bandwidth
Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training	DNN training	University of Michigan	NSDI 2023
The Design, Implementation and Evaluation of a Compiler Algorithm for GPU energy Reduction	Programming language; CPU energy	Rutgers, The State University of New Jersey	PLDI 2003
A Dynamic Compilation Framework for Controlling Microprocessor Energy and Performance	Compiler	Princeton University	MICRO 2005
Energy-Aware Non-Preemptive Task Scheduling With Deadline Constraint in DVFS-Enabled Heterogeneous Clusters	Task scheduling; Deadline Constraint	HKBU	TPDS 2022
EnvPipe: Performance-preserving DNN Training Framework for Saving Energy	Pipeline parallelism; Training	KAIST	ATC 2023
Power-aware Deep Learning Model Serving with μ-Serve	LLM Serving	UIUC	ATC 2024
DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency	LLM Serving Cluster	UIUC	Arxiv
Improving GPU Energy Efficiency through an Application-transparent Frequency Scaling Policy with Performance Assurance	Energy Efficiency	PCL,HKBU	Eurosys 2024
GPGPU Power Estimation with Core and Memory Frequency Scaling	estimate power with frequency	HKBU	ACM SIGMETRICS Performance Evaluation Review
GPGPU performance estimation with core	estimate performance with frequency	HKBU	TPDS 2020