# WANDER: An Explainable Decision-Support Framework for HPC

arXiv: https://arxiv.org/abs/2506.04049 · HTML: https://mlsanity.com/paper/2506.04049/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Foundations of Structural Causal Models with Cycles and Latent Variables](https://mlsanity.com/paper/1611.06221/) (2016)
- [Self-Supervised Learning for Recommender Systems: A Survey](https://mlsanity.com/paper/2203.15876/) (2022)
- [Decisions, Counterfactual Explanations and Strategic Behavior](https://mlsanity.com/paper/2002.04333/) (2020)
- [Recommendation with Generative Models](https://mlsanity.com/paper/2409.15173/) (2024)
- [Explaining a machine learning decision to physicians via counterfactuals](https://mlsanity.com/paper/2306.06325/) (2023)
- [Interpretable ECG Analysis for Myocardial Infarction Detection through Counterfactuals](https://mlsanity.com/paper/2312.08304/) (2023)
- [Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk](https://mlsanity.com/paper/2407.11089/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Distributed Framework for Causal Modeling of Performance Variability in GPU Traces](https://mlsanity.com/paper/2510.18300/) (2025)

## Abstract (arXiv)

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.