11 citations · 37 across the 21 of their papers we have counts for
25 papers
How to Relax Instantly: Elastic Relaxation of Concurrent Data Structures
Kåre von Geijer, Philippas Tsigas
The sequential semantics of many concurrent data structures, such as stacks and queues, inevitably lead to memory contention in parallel environments, thus limiting scalability. Se…
STRETCH: Virtual Shared-Nothing Parallelism for Scalable and Elastic Stream Processing
Vincenzo Gulisano, Hannaneh Najdataei, Yiannis Nikolakopoulos +3
Stream processing applications extract value from raw data through Directed Acyclic Graphs of data analysis tasks. Shared-nothing (SN) parallelism is the de-facto standard to scale…
Consistent Lock-free Parallel Stochastic Gradient Descent for Fast and Stable Convergence
Karl Bäckström, Ivan Walulya, Marina Papatriantafilou +1
Stochastic gradient descent (SGD) is an essential element in Machine Learning (ML) algorithms. Asynchronous parallel shared-memory SGD (AsyncSGD), including synchronization-free al…
Performance Modeling and Vertical Autoscaling of Stream Joins
Hannaneh Najdataei, Vincenzo Gulisano, Alessandro V. Papadopoulos +3
Streaming analysis is widely used in cloud as well as edge infrastructures. In these contexts, fine-grained application performance can be based on accurate modeling of streaming o…
MindTheStep-AsyncPSGD: Adaptive Asynchronous Parallel Stochastic Gradient Descent
Karl Bäckström, Marina Papatriantafilou, Philippas Tsigas
Stochastic Gradient Descent (SGD) is very useful in optimization problems with high-dimensional non-convex target functions, and hence constitutes an important component of several…
Relaxing Concurrent Data-structure Semantics for Increasing Performance: A Multi-structure 2D Design Framework
Adones Rukundo, Aras Atalar, Philippas Tsigas
There has been a significant amount of work in the literature proposing semantic relaxation of concurrent data structures for improving scalability and performance. By relaxing the…