[HTML][HTML] Energy-aware scheduling for high-performance computing systems: A survey
High-performance computing (HPC), according to its name, is traditionally oriented toward
performance, especially the execution time and scalability of the computations. However …
performance, especially the execution time and scalability of the computations. However …
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 …
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 …
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 …
An automated and portable method for selecting an optimal GPU frequency
Power consumption poses a significant challenge in current and emerging graphics
processing unit (GPU) enabled high-performance computing systems. In modern GPUs …
processing unit (GPU) enabled high-performance computing systems. In modern GPUs …
Sustaining performance while reducing energy consumption: a control theory approach
Production high-performance computing systems continue to grow in complexity and size.
As applications struggle to make use of increasingly heterogeneous compute nodes …
As applications struggle to make use of increasingly heterogeneous compute nodes …
Optimal GPU frequency selection using multi-objective approaches for hpc systems
Power consumption poses a significant challenge in current and emerging GPU-enabled
high-performance computing (HPC) systems. In modern GPUs, controls like dynamic voltage …
high-performance computing (HPC) systems. In modern GPUs, controls like dynamic voltage …
[HTML][HTML] JuMonC: A RESTful tool for enabling monitoring and control of simulations at scale
C Witzler, FSM Guimarães, D Mira, H Anzt… - Future Generation …, 2025 - Elsevier
As systems and simulations grow in size and complexity, it is challenging to maintain
efficient use of resources and avoid failures. In this scenario, monitoring becomes even …
efficient use of resources and avoid failures. In this scenario, monitoring becomes even …
A reinforcement learning approach for performance-aware reduction in power consumption of data center compute nodes
As Exascale computing becomes a reality, the energy needs of compute nodes in cloud data
centers will continue to grow. A common approach to reducing this energy demand is to limit …
centers will continue to grow. A common approach to reducing this energy demand is to limit …