WANDER: An Explainable Decision-Support Framework for HPC

TL;DR

WANDER is an explainable decision-support framework for HPC that synthesizes alternative configurations using counterfactual analysis, helping users explore, explain, and reconfigure systems to meet performance goals.

Contribution

WANDER uniquely unifies prediction, exploration, and explanation for HPC tuning within a single, interpretable framework using causal models and a composite trade-off score.

Findings

Generates human-readable configuration alternatives

Provides trustworthy and interpretable suggestions

Outperforms existing tools across multiple datasets

Abstract

High-performance computing (HPC) systems expose many interdependent configuration knobs that impact runtime, resource usage, power, and variability. Existing predictive tools model these outcomes, but do not support structured exploration, explanation, or guided reconfiguration. We present WANDER, a decision-support framework that synthesizes alternate configurations using counterfactual analysis aligned with user goals and constraints. We introduce a composite trade-off score that ranks suggestions based on prediction uncertainty, consistency between feature-target relationships using causal models, and similarity between feature distributions against historical data. To our knowledge, WANDER is the first such system to unify prediction, exploration, and explanation for HPC tuning under a common query interface. Across multiple datasets WANDER generates interpretable and trustworthy, human-readable alternatives that guide users to achieve their performance objectives.