Główne pojęcia
Efficiently deploying scientific workflows in HPC environments with provenance data capture using containerization strategies.
Streszczenie
The content introduces ProvDeploy, a framework for configuring containers for scientific workflows with integrated provenance data capture. It discusses challenges in deploying workflows in HPC environments and evaluates different containerization strategies using DenseED on SDumont CPUs and GPUs. The study explores the impact of strategies on execution time, CPU consumption, and performance.
Directory:
Abstract
Introduction
Background and Related Work
Containerization Principles
Provenance Services
Related Work Overview
ProvDeploy: Assisting the Deployment of Containerized Scientific Workflows in HPC Environments
Architecture of ProvDeploy
ProvDeploy in Action
Case Study: DenseED
Environment Setup
Exploring Different Containerization Strategies
Conclusion
Acknowledgments
References
Statystyki
"SDumont is a cluster with an installed processing capacity of around 5.1 Petaflop/s."
"Average CPU consumption for DenseED is 55% for the coarse-grained strategy."
"In GPUs, there is no statistical difference between the presented strategies."