Dynamic GPU energy optimization for machine learning training workloads
GPUs are widely used to accelerate the training of machine learning workloads. As modern
machine learning models become increasingly larger, they require a longer time to train …
machine learning models become increasingly larger, they require a longer time to train …
GPGPU performance estimation with core and memory frequency scaling
Contemporary graphics processing units (GPUs) support dynamic voltage and frequency
scaling to balance computational performance and energy consumption. However, accurate …
scaling to balance computational performance and energy consumption. However, accurate …
SYnergy: Fine-grained Energy-Efficient Heterogeneous Computing for Scalable Energy Saving
Energy-efficient computing uses power management techniques such as frequency scaling
to save energy. Implementing energy-efficient techniques on large-scale computing systems …
to save energy. Implementing energy-efficient techniques on large-scale computing systems …
A simple model for portable and fast prediction of execution time and power consumption of GPU kernels
L Braun, S Nikas, C Song, V Heuveline… - ACM Transactions on …, 2020 - dl.acm.org
Characterizing compute kernel execution behavior on GPUs for efficient task scheduling is a
non-trivial task. We address this with a simple model enabling portable and fast predictions …
non-trivial task. We address this with a simple model enabling portable and fast predictions …
Energy-aware non-preemptive task scheduling with deadline constraint in dvfs-enabled heterogeneous clusters
Energy conservation of large data centers for high performance computing workloads, such
as deep learning with Big Data, is of critical significance, where cutting down a few percent …
as deep learning with Big Data, is of critical significance, where cutting down a few percent …
Fine-grained powercap allocation for power-constrained systems based on multi-objective machine learning
Power capping is an important solution to keep the system within a fixed power constraint.
However, for the over-provisioned and power-constrained systems, especially the future …
However, for the over-provisioned and power-constrained systems, especially the future …
Online power management for multi-cores: A reinforcement learning based approach
Power and energy is the first-class design constraint for multi-core processors and is a
limiting factor for future-generation supercomputers. While modern processor design …
limiting factor for future-generation supercomputers. While modern processor design …
GPU static modeling using PTX and deep structured learning
In the quest for exascale computing, energy-efficiency is a fundamental goal in high-
performance computing systems, typically achieved via dynamic voltage and frequency …
performance computing systems, typically achieved via dynamic voltage and frequency …
Performance-aware energy-efficient GPU frequency selection using DNN-based models
Energy efficiency will be important in future accelerator-based HPC systems for
sustainability and to improve overall performance. This study proposes a deep neural …
sustainability and to improve overall performance. This study proposes a deep neural …
DSO: A GPU Energy Efficiency Optimizer by Fusing Dynamic and Static Information
Increased reliance on graphics processing units (GPUs) for high-intensity computing tasks
raises challenges regarding energy consumption. To address this issue, dynamic voltage …
raises challenges regarding energy consumption. To address this issue, dynamic voltage …