Publications by Darong Huang | EPFL Graph Search

A 20-Year Retrospective on Power and Thermal Modeling and Management

David Atienza Alonso, Darong Huang, Kai Zhu

As processor performance advances, increasing power densities and complex thermal behaviors threaten both energy efficiency and system reliability. This survey covers more than two decades of research on power and thermal modeling and management in modern ...

2025

Multi-Objective Management for Many-Core Computing Servers

Darong Huang

In recent decades, the rapid growth of cloud computing has transformed the technological landscape and profoundly impacted our daily lives, driven by the demand for secure, flexible, and cost-effective computing solutions. To meet these demands, computing ...

EPFL2025

Is the powersave governor really saving power?

David Atienza Alonso, Luis Maria Costero Valero, Darong Huang

A frequency scaling governor is critical for the performance management of cloud servers, as it enhances energy efficiency and helps to control operational temperatures, thereby ensuring system reliability. However, our in-depth analysis of the application ...

2024

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

David Atienza Alonso, Ali Pahlevan, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

2024

An Evaluation Framework for Dynamic Thermal Management Strategies in 3D MultiProcessor System-on-Chip Co-Design

David Atienza Alonso, Luis Maria Costero Valero, Darong Huang

Dynamic thermal management (DTM) has been widely adopted to improve the energy efficiency, reliability, and performance of modern MultiProcessor SoCs (MPSoCs) on runtime. However, the evolving industry trends and heterogeneous architecture designs have int ...

2024

Intermediate Address Space: virtual memory optimization of heterogeneous architectures for cache-resident workloads

David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang, Qunyou Liu

The increasing demand for computing power and the emergence of heterogeneous computing architectures have driven the exploration of innovative techniques to address current limitations in both the compute and memory subsystems. One such solution is the use ...

2024

REMOTE: Re-thinking Task Mapping on Wireless 2.5D Systems-on-Package for Hotspot Removal

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Darong Huang, Rafael Medina Morillas

2.5D Systems-on-Package (SoPs) are composed by several chiplets placed on an interposer. They are becoming increasingly popular as they enable easy integration of electronic components in the same package and high fabrication yields. Nevertheless, they int ...

2023

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

David Atienza Alonso, Ali Pahlevan, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang

Computing servers play a key role in the development and process of emerging compute-intensive applications in recent years. However, they need to operate efficiently from an energy perspective viewpoint, while maximizing the performance and lifetime of th ...

2022

COCKTAIL: Multi-Core Co-Optimization Framework With Proactive Reliability Management

David Atienza Alonso, Ali Pahlevan, Marina Zapater Sancho, Darong Huang

High performance computing (HPC) servers aim to meet an increase in the number and complexity of tasks and, consequently, to address the energy efficiency challenge. In addition to energy efficiency, it is essential to manage lifetime limitations of power- ...

2022

LSST: From Science Drivers to Reference Design and Anticipated Data Products

David Atienza Alonso, Guillaume Blanc, Shahram Khazaei, Christopher Montgomery, Darko Jevremovic, Abhijit Saha, Anja Annemone Becker, Michelle Miller, Jonathan Andrew Blazek, Darong Huang, Shikhar Tuli, Wei Cui

We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the solar system, ex ...

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