Concept
Scalability
Related publications (151)
Planetary-Scale Byzantine Fault Tolerance
The scale and pervasiveness of the Internet make it a pillar of planetary communication, industry and economy, as well as a fundamental medium for public discourse and democratic engagement. In stark contrast with the Internet's decentralized infrastructur ...
EPFL2024BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables
David Atienza Alonso, Miguel Peon Quiros, Pasquale Davide Schiavone, Rubén Rodríguez Álvarez, Denisa-Andreea Constantinescu, Dimitrios Samakovlis, Stefano Albini
The design of low-power wearables for the biomedical domain has received a lot of attention in recent decades, as technological advances in chip manufacturing have allowed real-time monitoring of patients using low-complexity ML within the mW range. Despit ...
2024Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science
Henry Markram, Sean Lewis Hill, Mohameth François Sy, Samuel Claude Kerrien, Carolina Johanna Elisabeth Lindqvist, Alejandra Garcia Rojas Martinez, Huanxiang Lu, Anna-Kristin Kaufmann, Jonathan Raël Lurie, Henry Genet, Pierre-Alexandre Fonta, Alexander Désiré Ulbrich, Michaël Fernand Paul Dupont, Silvia Rosario Jimenez Tejeda, Bogdan Roman, Ian Lavriushev, Didac Montero Mendez, Wojciech Adam Wajerowicz, Kenneth William Pirman, Julien Antonin Machon, Dhanesh Neela Mana, Natalia Stafeeva
Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simul ...
20232D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing
Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...
EPFL2023Special Session: Challenges and Opportunities for Sustainable Multi-Scale Computing Systems
David Atienza Alonso, Miguel Peon Quiros
Multi-Scale computing systems aim at bringing the computing as close as possible to the data sources, to optimize both computation and networking. These systems are composed of at least three computing layers: the terminal layer, the edge layer, and the cl ...
ACM2023Building Chips Faster: Hardware-Compiler Co-Design for Accelerated RTL Simulation
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 redefined ...
EPFL2023Building Strongly-Consistent Systems Resilient to Failures, Partitions, and Slowdowns
Distributed systems designers typically strive to improve performance and preserve availability despite failures or attacks; but, when strong consistency is also needed, they encounter fundamental limitations. The bottleneck is in replica coordination, whi ...
EPFL2023Skadi: Building a Distributed Runtime for Data Systems in Disaggregated Data Centers
Data-intensive systems are the backbone of today's computing and are responsible for shaping data centers. Over the years, cloud providers have relied on three principles to maintain cost-effective data systems: use disaggregation to decouple scaling, use ...
New York2023The Universal Gossip Fighter
Rachid Guerraoui, Anne-Marie Kermarrec, Anastasiia Kucherenko, Rafaël Benjamin Pinot
The notion of adversary is a staple of distributed computing. An adversary typically models “hostile” assumptions about the underlying distributed environment, e.g., a network that can drop messages, an operating system that can delay processes or an attac ...
2022Peer-Prediction in the Presence of Outcome Dependent Lying Incentives
Boi Faltings, Naman Goel, Aris Filos Ratsikas
We derive conditions under which a peer-consistency mechanism can be used to elicit truthful data from non-trusted rational agents when an aggregate statistic of the collected data affects the amount of their incentives to lie. Furthermore, we discuss the ...
2022