Concept

MapReduce

Publications associées (41)

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

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

Scalable Robust Graph Embedding with Spark

Karl Aberer, Quoc Viet Hung Nguyen, Chi Thang Duong, Trung-Dung Hoang

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to larg ...

ASSOC COMPUTING MACHINERY2021

Communication trade-offs for Local-SGD with large step size

Aymeric Daphnis Kévin Dieuleveut

Synchronous mini-batch SGD is state-of-the-art for large-scale distributed machine learning. However, in practice, its convergence is bottlenecked by slow communication rounds between worker nodes. A natural solution to reduce communication is to use the " ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Formal Approaches to Querying Big Data in Shared-Nothing Systems

Bas Ketsman

To meet today's data management needs, it is a widespread practice to use distributed data storage and processing systems. Since the publication of the MapReduce paradigm, a plethora of such systems arose, but although widespread, the capabilities of these ...

ASSOC COMPUTING MACHINERY2019

System Support for Efficient Replication in Distributed Systems

Dragos-Adrian Seredinschi

Current online applications, such as search engines, social networks, or file sharing services, execute across a distributed network of machines. They provide non-stop services to their users despite failures in the underlying network. To achieve such a hi ...

EPFL2019

An accelerated communication-efficient primal-dual optimization framework for structured machine learning

Martin Jaggi, Chenxin Ma

Distributed optimization algorithms are essential for training machine learning models on very large-scale datasets. However, they often suffer from communication bottlenecks. Confronting this issue, a communication-efficient primal-dual coordinate ascent ...

2019

Towards Real-Time High-Resolution Interferometric Imaging with Bluebild

Sepand Kashani

Rooted in radio-astronomy, the Bluebild algorithm is a paradigm-changing imager to estimate the sky intensity function. However, current imaging pipelines in radio-astronomy are highly-tailored to the CLEAN-family of imagers, and hence lack the flexibility ...

2017

Compilation Techniques for Incremental Collection Processing

Daniel Lupei

Many map-reduce frameworks as well as NoSQL systems rely on collection programming as their interface of choice due to its rich semantics along with an easily parallelizable set of primitives. Unfortunately, the potential of collection programming is not ...

EPFL2017

An origami-inspired cargo drone

Dario Floreano, Stefano Mintchev, Przemyslaw Mariusz Kornatowski

Multicopters stand to revolutionize parcel delivery because of their capability to operate in areas with unsuitable road infrastructure and precisely maneuver in cluttered environments. However, current multicopters for delivery can be dangerous for people ...

2017

Optimized Distributed Hyperparameter Search and Simulation for Lung Texture Classification in CT Using Hadoop

Adrien Raphaël Depeursinge

Many medical image analysis tasks require complex learning strategies to reach a quality of image-based decision support that is sufficient in clinical practice. The analysis of medical texture in tomographic images, for example of lung tissue, is no excep ...

2016

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