Publications related to Multiple instruction, multiple data

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks

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

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

STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale

Felix Schürmann, James Gonzalo King, Pramod Shivaji Kumbhar, Omar Awile, Christos Kotsalos, Tristan Mathieu Carel, Iain Hepburn, Samuel Marie A Melchior, Weiliang Chen, Baudouin Paul Michel Maria Joseph Del Marmol, Alessandro Cattabiani, Giacomo Castiglioni, Nicola Cantarutti

Recent advances in computational neuroscience have demonstrated the usefulness and importance of stochastic, spatial reaction-diffusion simulations. However, ever increasing model complexity renders traditional serial solvers, as well as naive parallel imp ...

FRONTIERS MEDIA SA2022

High-order accurate entropy stable adaptive moving mesh finite difference schemes for special relativistic (magneto)hydrodynamics

Junming Duan

This paper develops high-order accurate entropy stable (ES) adaptive moving mesh finite difference schemes for the two- and three-dimensional special relativistic hydrodynamic (RHD) and magnetohydrodynamic (RMHD) equations, which is the high-order accurate ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

POSEIDON: Privacy-Preserving Federated Neural Network Learning

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

An Architecture for Load Balance in Computer Cluster Applications

Laurent Bindschaedler

Amid a data revolution that is transforming industries around the globe, computing systems have undergone a paradigm shift where many applications are scaled out to run on multiple computers in a computing cluster. As the storage and processing capabilitie ...

EPFL2020

The SONATA data format for efficient description of large-scale network models

Eilif Benjamin Muller, James Gonzalo King, Adrien Michel Achille Devresse, Werner Alfons Hilda Van Geit, Jean-Denis Georges Emile Courcol, Juan Bautista Hernando Vieites, Michael Emiel Gevaert, Judit Planas Carbonell

Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation ...

2020

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 ...

OUP2016

Characterization of the Impact of Hardware Islands on OLTP

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

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

FPGAs for the Masses: Affordable Hardware Synthesis from Domain-Specific Languages

Nithin George

Field Programmable Gate Arrays (FPGAs) have the unique ability to be configured into application-specific architectures that are well suited to specific computing problems. This enables them to achieve performances and energy efficiencies that outclass oth ...

EPFL2016

Femtosecond laser-based production of 3D micro- and nano-devices in transparent substrate: a step toward system-materials

Yves Bellouard

Femtosecond laser irradiation applied to Fused Silica (the amorphous form of SiO2) defines a novel technology platform for highly integrated all-optical microsystems and beyond. In contrast with common approaches that rely on combining materials to achieve ...

Spie-Int Soc Optical Engineering2015

QMMMW: A wrapper for QM/MM simulations with QUANTUM ESPRESSO and LAMMPS

Changru Ma

We present QMMMW, a new program aimed at performing Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics. The package operates as a wrapper that patches PWscf code included in the QUANTUM ESPRESSO distribution and LAMMPS Molecular Dynamics Simu ...

Elsevier Science Bv2015

turboTDDFT 2.0 Hybrid functionals and new algorithms within time-dependent density-functional perturbation theory

We present a new release of the turboTDDFT code featuring an implementation of hybrid functionals, a recently introduced pseudo-Hermitian variant of the Liouville-Lanczos approach to time-dependent density-functional perturbation theory, and a newly develo ...

Elsevier Science Bv2014

Dynamic Fine-Grained Scheduling for Energy-Efficient Main-Memory Queries

Anastasia Ailamaki, Pinar Tözün, Danica Porobic, Iraklis Psaroudakis, Erietta Liarou

Power and cooling costs are some of the highest costs in data centers today, which make improvement in energy efficiency crucial. Energy efficiency is also a major design point for chips that power whole ranges of computing devices. One important goal in t ...

2014

Ultra-Low Power Multicore Architecture For Parallel Biomedical Signal Processing

David Atienza Alonso, Andreas Peter Burg, Ahmed Yasir Dogan, Jeremy Hugues-Felix Constantin

A multi-core architecture with ultra-low power consumption is needed for a wide variety of applications, especially in the bio-medical domain. In this patent, an ultra-low power multi-core architecture is presented: it is composed of one or more cores, sev ...

2014