Wattlytics: A Web Platform for Co-Optimizing Performance, Energy, and TCO in HPC Clusters

TL;DR

Wattlytics is an interactive web platform that helps optimize HPC cluster performance, energy use, and total cost of ownership by integrating benchmarking, power modeling, and scenario analysis.

Contribution

It introduces Wattlytics, a novel decision-support system combining performance scaling, power modeling, and TCO analysis in a single environment for HPC cluster design.

Findings

Optimally deployed energy-efficient GPUs can outperform higher-performance GPUs under certain constraints.

Wattlytics enables exploration of design parameters, cost factors, and risk analysis for HPC clusters.

Case studies demonstrate the platform's utility in making cost-effective and energy-efficient deployment decisions.

Abstract

The escalating computational demands and energy footprint of GPU-accelerated computing systems complicate informed design and operational decisions. We present the first release of Wattlytics (https://wattlytics.netlify.app), an interactive, browser-based decision-support system. Unlike existing procurement-oriented calculators, Wattlytics uniquely integrates benchmark-driven GPU performance scaling, dynamic voltage and frequency scaling (DVFS)-aware piecewise power modeling, and multi-year total cost of ownership (TCO) analysis within a single interactive environment. Users can configure heterogeneous systems across contemporary GPU architectures (GH200, H100, L40S, L40, A40, A100, and L4), select representative scientific workloads (e.g., GROMACS, AMBER), and explore deployment scenarios under constraints such as energy prices, system lifetime, and frequency scaling. Wattlytics computes multidimensional decision metrics (TCO breakdown, work-per-TCO, power-per-TCO, and work-per-watt-per-TCO) and supports design-space exploration, what-if scenarios, sensitivity metrics (elasticity, Sobol indices, Monte Carlo) and collaborative features to guide realistic cluster design and procurement under uncertainty. We demonstrate selected scenarios comparing deployment strategies under different operational modes: ixed budget, fixed GPU count, fixed performance, and fixed power. Our case studies show that, under budget or energy constraints, optimally deployed energy-efficient GPUs can outperform higher-performance alternatives in overall cost-effectiveness. Wattlytics helps users explore the design parameter space and distinguish between cost- and risk-driving factors, turning HPC design into a well-informed and explainable decision-making process.