research
∙
08/29/2023
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
Recently, graph neural networks (GNNs) have gained much attention as a g...
share
research
∙
06/08/2023
FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems
Federated Machine Learning (FL) has received considerable attention in r...
research
∙
06/05/2023
How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study
Training deep learning models in the cloud or on dedicated hardware is e...
research
∙
05/23/2023
The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey
Graph Neural Networks (GNNs) are an emerging research field. This specia...
research
∙
05/05/2023
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
The aim of dataset distillation is to encode the rich features of an ori...
research
∙
05/03/2023
A Survey on Dataset Distillation: Approaches, Applications and Future Directions
Dataset distillation is attracting more attention in machine learning as...
research
∙
04/11/2023
Partitioner Selection with EASE to Optimize Distributed Graph Processing
For distributed graph processing on massive graphs, a graph is partition...
research
∙
01/11/2023
How To Optimize My Blockchain? A Multi-Level Recommendation Approach
Aside from the conception of new blockchain architectures, existing bloc...
research
∙
10/04/2022
OrderlessChain: Do Permissioned Blockchains Need Total Global Order of Transactions?
Existing permissioned blockchains often rely on coordination-based conse...
research
∙
06/23/2022
The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data
The DEBS Grand Challenge (GC) is an annual programming competition open ...
research
∙
03/23/2022
Out-of-Core Edge Partitioning at Linear Run-Time
Graph edge partitioning is an important preprocessing step to optimize d...
research
∙
02/17/2022
Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines
Preprocessing pipelines in deep learning aim to provide sufficient data ...
research
∙
03/23/2021
Hybrid Edge Partitioner: Partitioning Large Power-Law Graphs under Memory Constraints
Distributed systems that manage and process graph-structured data intern...
research
∙
03/08/2021
Why Do My Blockchain Transactions Fail? A Study of Hyperledger Fabric (Extended version)*
Permissioned blockchain systems promise to provide both decentralized tr...
research
∙
01/20/2020
2PS: High-Quality Edge Partitioning with Two-Phase Streaming
Graph partitioning is an important preprocessing step to distributed gra...
research
∙
03/27/2019
Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools
Deep Learning (DL) has had an immense success in the recent past, leadin...
research
∙
01/28/2019
A Comprehensive Survey on Parallelization and Elasticity in Stream Processing
Stream Processing (SP) has evolved as the leading paradigm to process an...
research
∙
10/26/2018
HYPE: Massive Hypergraph Partitioning with Neighborhood Expansion
Many important real-world applications-such as social networks or distri...
research
∙
06/12/2018
P4CEP: Towards In-Network Complex Event Processing
In-network computing using programmable networking hardware is a strong ...
research
∙
05/30/2018
Q-Graph: Preserving Query Locality in Multi-Query Graph Processing
Arising user-centric graph applications such as route planning and perso...
research
∙
12/22/2017
ADWISE: Adaptive Window-based Streaming Edge Partitioning for High-Speed Graph Processing
In recent years, the graph partitioning problem gained importance as a m...
research
∙
11/06/2017
The TensorFlow Partitioning and Scheduling Problem: It's the Critical Path!
State-of-the-art data flow systems such as TensorFlow impose iterative c...
research
∙
10/13/2017
