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Concept

Measuring network throughput

Publications associées (31)

High-Throughput and Flexible Belief Propagation List Decoder for Polar Codes

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Yifei Shen, Yuqing Ren, Andreas Toftegaard Kristensen, Leyu Zhang

Due to its high parallelism, belief propagation (BP)decoding is amenable to high-throughput applications and thusrepresents a promising solution for the ultra-high peak datarate required by future communication systems. To bridge theperformance gap compare ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Cooperative Concurrency Control for Write-Intensive Key-Value Workloads

Babak Falsafi, Alexandros Daglis, Mark Johnathon Sutherland

Key-Value Stores (KVS) are foundational infrastructure components for online services. Due to their latency-critical nature, today’s best-performing KVS contain a plethora of full-stack optimizations commonly targeting read-mostly, popularity-skewed worklo ...

ACM2023

Ultra-Low Power Short-Range 60-GHz FMCW Radar Front-End

Sammy Cerida Rengifo

The millimeter wave (mmWave) frequency band is becoming very interesting over the past years because it provides the opportunity to operate using large bandwidths which are not available at lower frequencies. This enables new communication standards suppor ...

EPFL2023

Towards high-throughput many-body perturbation theory: efficient algorithms and automated workflows

Giovanni Pizzi, Antimo Marrazzo, Junfeng Qiao

The automation of ab initio simulations is essential in view of performing high-throughput (HT) computational screenings oriented to the discovery of novel materials with desired physical properties. In this work, we propose algorithms and implementations ...

NATURE PORTFOLIO2023

Localizing Traffic Differentiation

Aikaterini Argyraki, Pavlos Nikolopoulos, Muhammad Abdullah, Zeinab Shmeis

Network neutrality is important for users, content providers, policymakers, and regulators interested in understanding how network providers differentiate performance. When determining whether a network differentiates against certain traffic, it is importa ...

2023

An Iterative Method for Mapping-Aware Frequency Regulation in Dataflow Circuits

Andrea Guerrieri, Lana Josipovic

Dataflow circuits promise to overcome the scheduling limitations of standard HLS solutions. However, their performance suffers due to timing overheads caused by their handshake communication protocol. Current pipelining solutions fail to account for logic ...

IEEE2023

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

Florent Gérard Krzakala

Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 11 ) parameters, are trained on O(10 11 ) words and produce awe-inspiring resu ...

IEEE2023

Genuinely distributed Byzantine machine learning

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault, Le Nguyen Hoang, Arsany Hany Abdelmessih Guirguis

Machine learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

2022

Efficient Analytical Query Processing on CPU-GPU Hardware Platforms

Periklis Chrysogelos

Timely insights lead to business growth and scientific breakthroughs but require analytical engines that cope with the ever-increasing data processing needs. Analytical engines relied on rapid CPU improvements, yet the end of Dennard scaling stopped the fr ...

EPFL2022

High-throughput Microfluidic Platforms for S. cerevisiae Single Cell Studies

Hon Ming Andrew Yip

Cells consume extra-cellular nutrients and resources to maintain cellular fitness. Extra-cellular conditions vary over time. Cellular programs encoded in genes adjust to adapt to the environments. Gene regulatory networks (GRNs) have evolved to be responsi ...

EPFL2022

High-Throughput Sizing, Counting, and Elemental Analysis of Anisotropic Multimetallic Nanoparticles with Single-Particle Inductively Coupled Plasma Mass Spectrometry

Andreas Züttel, Natalia Gasilova, Wen Luo, Mo Li, Cedric David Koolen

Nanoparticles (NPs) have wide applications in physical and chemical processes, and their individual properties (e.g., shape, size, and composition) and ensemble properties (e.g., distribution and homogeneity) can significantly affect the performance. Howev ...

2022

Synthesis and direct assay of large macrocycle diversities by combinatorial late-stage modification at picomole scale

Christian Heinis, Gerardo Turcatti, Alessandro Angelini, Jonathan Patrice Vesin, Julien Bortoli Chapalay, Manuel Leonardo Merz, Sevan Mleh Habeshian, Mischa Schüttel, Ganesh Kumar Mothukuri, Gontran Sangouard, Cristina Diaz Perlas, Zsolt Bognár

Macrocycles have excellent potential as therapeutics due to their ability to bind challenging targets. However, generating macrocycles against new targets is hindered by a lack of large macrocycle libraries for high-throughput screening. To overcome this, ...

Nature Research2022

GARFIELD: System Support for Byzantine Machine Learning (Regular Paper)

Rachid Guerraoui, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

We present GARFIELD, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine–resilient. GARFIELD relies on a novel object–oriented design, reducing t ...

IEEE2021

High-Throughput Lossy-to-Lossless 3D Image Compression

Gilles Fourestey, Félix Schmidt

The rapid increase in medical and biomedical image acquisition rates has opened up new avenues for image analysis, but has also introduced formidable challenges. This is evident, for example, in selective plane illumination microscopy where acquisition rat ...

Institute of Electrical and Electronics Engineers2021

Resource Trade-Offs in Circuits and Systems: from Neurotechnology to Communications

Arda Uran

This thesis aims to explore and exploit trade-offs in integrated circuits and systems to overcome the fundamental bottlenecks faced by future data acquisition and communication systems. Specifically, we target the emerging implantable neurotechnology and t ...

EPFL2021

A Simpler and Faster NIC Driver Model for Network Functions

George Candea, Solal Vincenzo Pirelli

The advent of software network functions calls for stronger correctness guarantees and higher performance at every level of the stack. Current network stacks trade simplicity for performance and flexibility, especially in their driver model. We show that p ...

2021

Developing new tools to study and program cell fate at the single-cell level

Marjan Biocanin

Single-cell transcriptomics (scRNA-seq) started a technological revolution in biology by enabling through the plethora of methods to assess a molecular state of the cell on systems level without the strict necessity of the prior knowledge of the cell state ...

EPFL2021

A Maximum-Likelihood-based Multi-User LoRa Receiver Implemented in GNU Radio

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Orion Afisiadis, Joachim Tobias Tapparel

LoRa is a popular low-power wide-area network (LPWAN) technology that uses spread-spectrum to achieve long-range connectivity and resilience to noise and interference. For energy efficiency reasons, LoRa adopts a pure ALOHA access scheme, which leads to re ...

IEEE2020

Hailstorm: Disaggregated Compute and Storage for Distributed LSM-based Databases

Willy Zwaenepoel, Laurent Bindschaedler

Distributed LSM-based databases face throughput and latency issues due to load imbalance across instances and interference from background tasks such as flushing, compaction, and data migration. Hailstorm addresses these problems by deploying the database ...

ACM2020

Genuinely Distributed Byzantine Machine Learning

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault, Le Nguyen Hoang, Arsany Hany Abdelmessih Guirguis

Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

Association for Computing Machinery2020