Concept

Multiple instruction, multiple data

Related publications (40)

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks

Martin Jaggi, Mary-Anne Hartley, Vinitra Swamy, Jibril Albachir Frej, Thierry Bossy, Thijs Vogels, Malika Satayeva

Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space. Multimodal (MM) models aim to extract the synergistic predictive potential of multiple data types to create a shared feature space with aligned sema ...

2023

Post-Moore's Law Fusion: High-Bandwidth Memory, Accelerators, and Native Half-Precision Processing for CPU-Local Analytics

Anastasia Ailamaki, Viktor Sanca

Modern data management systems aim to provide both cutting-edge functionality and hardware efficiency. With the advent of AI-driven data processing and the post-Moore Law era, traditional memory-bound scale-up data management operations face scalability ch ...

2023

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, Apostolos Pyrgelis, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Sinem Sav

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

INTERNET SOC2021

Extreme Scalability of DFT-Based QM/MM MD Simulations Using MiMiC

Ursula Röthlisberger, Simone Meloni, Martin Peter Bircher, Viacheslav Bolnykh, Jógvan Magnus Olsen, Paolo Carloni

We present a highly scalable DFT-based QM/MM implementation developed within MiMiC, a recently introduced multiscale modeling framework that uses a loose-coupling strategy in conjunction with a multiple-program multiple-data (MPMD) approach. The computatio ...

2019

MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry

Ursula Röthlisberger, Simone Meloni, Martin Peter Bircher, Viacheslav Bolnykh, Paolo Carloni

We present a flexible and efficient framework for multiscale modeling in computational chemistry (MiMiC). It is based on a multiple-program multiple-data (MPMD) model with loosely coupled programs. Fast data exchange between programs is achieved through th ...

AMER CHEMICAL SOC2019

Network-Compute Co-Design for Distributed In-Memory Computing

Alexandros Daglis

The booming popularity of online services is rapidly raising the demands for modern datacenters. In order to cope with data deluge, growing user bases, and tight quality of service constraints, service providers deploy massive datacenters with tens to hund ...

EPFL2018

Heterogeneous and Inexact: Maximizing Power Efficiency of Edge Computing Sensors for Health Monitoring Applications

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Soumya Subhra Basu, Loris Gérard Duch

In the Internet-of-Things (IoT) era, there is an increasing trend to enable intelligent behavior in edge computing sensors. Thus, a new generation of smart wearable devices for health monitoring is being developed, able to perform complex Digital Signal Pr ...

IEEE2018

Efficient Adaptive Hard Real-time Multi-processor Systems

Stefanos Skalistis

Modern computing systems are based on multi-processor systems, i.e. multiple cores on the same chip. Hard real-time systems are required to perform particular tasks within certain amount of time; failure to do so characterises an unaccepted behavior. Hard ...

EPFL2017

Scalable splitting algorithms for big-data interferometric imaging in the SKA era

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In the context of next generation radio telescopes, like the Square Kilometre Array, the efficient processing of large-scale datasets is extremely important. Convex optimisation tasks under the compressive sensing framework have recently emerged and provid ...

Wiley-Blackwell2016

Characterization of the Impact of Hardware Islands on OLTP

Anastasia Ailamaki, Miguel Sérgio De Oliveira Branco, Pinar Tözün, Ippokratis Pandis, Danica Porobic

Modern hardware is abundantly parallel and increasingly heterogeneous. The numerous processing cores have non-uniform access latencies to the main memory and processor caches, which causes variability in the communication costs. Unfortunately, database sys ...

Springer Verlag2016

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.