ClusterBench: A Framework for Cluster-Wide Continuous Benchmarking and Regression Testing

2026-08-11

AI Summary

This work addresses the lack of existing tools capable of continuous performance validation and regression detection across entire datacenter clusters. The authors propose the first cluster-wide continuous benchmarking framework that supports unified scheduling, enabling simultaneous task distribution to all nodes and systematic collection of multidimensional performance metrics—spanning CPU, GPU, memory, interconnects, I/O, power consumption, frequency, and temperature—across both space and time. This framework facilitates performance regression detection under software and hardware changes as well as analysis of hardware variability. Experiments on the NHR@FAU cluster reveal intra-node performance variations below 1% among identically configured nodes, while inter-node differences reach up to 5%. The study further uncovers, for the first time, significant disparities in the performance–power relationship between air-cooled and liquid-cooled nodes.

Abstract

Data centers need tooling that validates an entire installation rather than individual nodes, at acceptance and at regular intervals thereafter. This requires dispatching identical benchmarks to every node in a single submission, and therefore cluster-aware scheduling. This paper presents ClusterBench, a framework for cluster-wide continuous benchmarking. It ships with a benchmark collection targeting each component: CPU, GPU, memory, interconnect, and I/O. Because measurements are repeated throughout the cluster's lifetime, ClusterBench collects data across space and time. Comparison against earlier runs detects performance regressions introduced by software changes, such as kernel updates or new library versions. The measurements also form a dataset for research on hardware variability. On the NHR@FAU clusters Helma, Alex, and Fritz, variation within a single component stays within 1%. Variation across specimens reaches 5%, despite nodes identical by specification. Correlating performance with power draw, frequency, and temperature shows that this relationship differs between air- and liquid-cooled nodes.

Problem

• cluster-wide benchmarking
• performance regression
• hardware variability
• continuous testing
• data center validation

Innovation

• cluster-wide benchmarking
• regression testing
• hardware variability
• cluster-aware scheduling
• continuous performance monitoring