4 citations · 5 across the 4 of their papers we have counts for
4 papers
Stage-Level Executor Allocation in Apache Spark with Cost-Performance Trade-offs
Miriam Rateike, Isaac Waweru Wambugu, Celia Cintas +3
Allocating executors (i.e. compute resources) to distributed processing systems must balance resource costs of scaling-out unnecessarily against artificial, performance-limiting bo…
Efficient and Accurate In-Database Machine Learning with SQL Code Generation in Python
Michael Kaufmann, Gabriel Stechschulte, Anna Huber
Following an analysis of the advantages of SQL-based Machine Learning (ML) and a short literature survey of the field, we describe a novel method for In-Database Machine Learning (…
Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle
Michael Kaufmann, Kornilios Kourtis, Celestine Mendler-Dünner +2
Distributed machine learning training is one of the most common and important workloads running on data centers today, but it is rarely executed alone. Instead, to reduce costs, co…
Elastic CoCoA: Scaling In to Improve Convergence
Michael Kaufmann, Thomas Parnell, Kornilios Kourtis
In this paper we experimentally analyze the convergence behavior of CoCoA and show, that the number of workers required to achieve the highest convergence rate at any point in time…