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Concept

Test de performance

Publications associées (30)

Is the powersave governor really saving power?

David Atienza Alonso, Darong Huang, Luis Maria Costero Valero

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

Oligolithic Cross-task Optimizations across Isolated Workloads

Anastasia Ailamaki, Panagiotis Sioulas, Eleni Zapridou

Enterprises collect data in large volumes and leverage them to drive numerous concurrent decisions and business processes. Their teams deploy multiple applications that often operate concurrently on the same data and infrastructure but have widely differen ...

2024

Learning a QoE Metric from Social Media and Gaming Footage

Aikaterini Argyraki, Catalina Paz Alvarez Inostroza

Defining a universal metric for Quality of Experience (QoE) is notoriously hard due to the complex relationship between low-level performance metrics and user satisfaction. The most common metric, the Mean Opinion Score (MOS), has well-known biases and inc ...

2023

Building Chips Faster: Hardware-Compiler Co-Design for Accelerated RTL Simulation

Sahand Kashani

The demise of Moore's Law and Dennard scaling has resulted in diminishing performance gains for general-purpose processors, and so has prompted a surge in academic and commercial interest for hardware accelerators. Specialized hardware has already redefine ...

EPFL2023

Gradient boosting with extreme-value theory for wildfire prediction

Jonathan Koh Boon Han

This paper details the approach of the team Kohrrelation in the 2021 Extreme Value Analysis data challenge, dealing with the prediction of wildfire counts and sizes over the contiguous US. Our approach uses ideas from extreme-value theory in a machine lear ...

2023

Efficient Concurrent Analytical Query Processing using Data and Workload-conscious Sharing

Panagiotis Sioulas

Analytical workloads are evolving as the number of users surges and applications that submit queries in batches become popular. However, traditional analytical databases that optimize-then-execute each query individually struggle to provide timely response ...

EPFL2023

Performance Interfaces for Network Functions

George Candea, Aikaterini Argyraki, Rishabh Ramesh Iyer

Modern programmers routinely use third-party code, and infrastructure operators deploy software they did not write. This would not be possible without semantic interfaces---documentation, header files, specifications---that succinctly describe what that th ...

USENIX Association2022

Development of a large-area, light-weight module using the MALTA monolithic pixel detector

Edoardo Charbon, Francesco Piro

The MALTA pixel chip is a 2 cm x 2 cm large monolithic pixel detector developed in the Tower 180 nm imaging process. The chip contains four CMOS transceiver blocks at its sides which allow chip-to-chip data transfer. The power pads are located mainly at th ...

ELSEVIER2022

Two-layer adaptive signal control framework for large-scale networks combining efficient Max-Pressure and Perimeter Control

Nikolaos Geroliminis, Anastasios Kouvelas, Dimitrios Tsitsokas

This work investigates the benefits of a two-layer adaptive signal control framework combining multi-region perimeter control (PC) with distributed Max Pressure (MP) control in selected network intersections. Motivated by MP’s questionable performance in o ...

2022

Consensus Control for Decentralized Deep Learning

Martin Jaggi, Sebastian Urban Stich, Tao Lin, Anastasiia Koloskova

Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers fro ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?

Alexandre Massoud Alahi, Yuejiang Liu, Taylor Ferdinand Mordan, Parth Ashit Kothari, Bastien Germain C. Van Delft

Test-time training (TTT) through self-supervised learning (SSL) is an emerging paradigm to tackle distributional shifts. Despite encouraging results, it remains unclear when this approach thrives or fails. In this work, we first provide an in-depth look at ...

2021

Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Martin Jaggi, Sebastian Urban Stich, Tao Lin, Sai Praneeth Reddy Karimireddy

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization ch ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Lupulus: A Flexible Hardware Accelerator For Neural Networks

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Robert Giterman, Andreas Toftegaard Kristensen

Neural networks have become indispensable for a wide range of applications, but they suffer from high computational- and memory-requirements, requiring optimizations from the algorithmic description of the network to the hardware implementation. Moreover, ...

IEEE2020

An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition

Mathew Magimai Doss, Sandrine Tornay

HMMs have been the one of the first models to be applied for sign recognition and have become the baseline models due to their success in modeling sequential and multivariate data. Despite the extensive use of HMMs for sign recognition, determining the HMM ...

EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA2020

Design and development of the efficient anguilliform swimming robot- MAR

Auke Ijspeert, Peter Eckert, Mohammadreza Bayat

Propulsion of swimming robots at the surface and underwater is largely dominated by rotary propellers due to high thrust, but at the cost of low efficiency. Due to their inherently high speed turning motion, sharp propeller blades and generated noise, they ...

2020

2-D Materials for Ultrascaled Field-Effect Transistors: One Hundred Candidates under the Ab Initio Microscope

Nicola Marzari, Davide Campi

Due to their remarkable properties, single-layer 2-D materials appear as excellent candidates to extend Moore's scaling law beyond the currently manufactured silicon FinFETs. However, the known 2-D semiconducting components, essentially transition metal di ...

AMER CHEMICAL SOC2020

Degradation analysis in mixed (MAPbI(3) and MAPbBr(3)) perovskite solar cells under thermal stress

Mohammad Khaja Nazeeruddin, Sanghyun Paek

The current study provides an insight into the thermal stability of perovskite solar cells (PSCs) and the factors causing their degradation. To this end, a systematic stability study was carried out on n-i-p type perovskite solar cells involving mesoporous ...

SPRINGER2019

Taster: Self-Tuning, Elastic and Online Approximate Query Processing

Anastasia Ailamaki, Raja Appuswamy, Matthaios Alexandros Olma, Odysseas Papapetrou

Current Approximate Query Processing (AQP) engines are far from silver-bullet solutions, as they adopt several static design decisions that target specific workloads and deployment scenarios. Offline AQP engines target deployments with large storage budget ...

IEEE2019

Lock-Unlock: Is That All? A Pragmatic Analysis of Locking in Software Systems

Rachid Guerraoui, Vasileios Trigonakis

A plethora of optimized mutex lock algorithms have been designed over the past 25 years to mitigate performance bottlenecks related to critical sections and locks. Unfortunately, there is currently no broad study of the behavior of these optimized lock alg ...

2019

Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures

We propose an online algorithm for sequential learning in the contextual multiarmed bandit setting. Our approach is to partition the context space and, then, optimally combine all of the possible mappings between the partition regions and the set of bandit ...

2019