Campaign Knowledge Network: Building Knowledge for Campaign Efficiency

TL;DR

This paper introduces the Campaign Knowledge Network (CKN), a tool designed to capture and represent knowledge from collaborative exascale computing campaigns, enhancing efficiency and analysis of experimental activities.

Contribution

The paper presents CKN, a novel co-design tool that captures campaign context from runtime data to improve experiment analysis and efficiency.

Findings

CKN satisfies the Hoarde abstraction.

CKN effectively distills campaign context from runtime information.

CKN creates a valuable knowledge resource for analysis tools.

Abstract

In the landscape of exascale computing collaborative research campaigns are conducted as co-design activities of loosely coordinated experiments. But the higher level context and the knowledge of individual experimental activity is lost over time. We undertook a knowledge capture and representation aid called Campaign Knowledge Network(CKN), a co-design design and analysis tool. We demonstrate that CKN can satisfy the Hoarde abstraction and can distill campaign context from runtime information thereby creating a knowledge resource upon which analysis tools can run to provide more efficient experimentation