Visualizing The Linux Kernel Performance with FlameGraph AR

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dc.catalogadorjca
dc.contributor.authorRojas-Stambuk, Tiara
dc.contributor.authorGil-Gareca, Luis Fernando
dc.contributor.authorSandoval Alcocer, Juan Pablo
dc.contributor.authorMerino Del Campo, Leonel Alejandro
dc.contributor.authorMoreno-Lumbreras, David
dc.date.accessioned2025-12-09T13:46:54Z
dc.date.available2025-12-09T13:46:54Z
dc.date.issued2025
dc.description.abstractIn this challenge, we explore the evolution of the Linux kernel’s performance during compilation by comparing versions 5.19.17 and 6.14 through sampling-based CPU profiling. We collect profiling data using perf, transform into Chromecompatible .cpuprofile format, and analyze through a novel spatial visualization called FlameGraph AR. FlameGraph AR extends traditional flamegraphs beyond the limitations of IDE panels and conventional screens by rendering visualizations with augmented reality on a Microsoft HoloLens 2 device. By offloading the flamegraph to physical space, theFlameGraph AR tool enables developers to walk through wide and deeply nested call stacks, examine function frames through gesture-based interactions, and gain spatial awareness of the runtime behavior of a software system. In effect, we found immersive visualization especially valuable for analyzing architectural changes between the two kernel versions. We found that version 6.14 exhibits a significantly higher number of samples in several functions, such asnative_write_msr, indicating intensified low-level CPU interactions. In addition, functions such as intel_pmu_enable_alland x86_pmu_enable also increased in frequency, suggesting increased reliance on performance monitoring. The stack depth analysis revealed that certain functions in version 6.14, includingfpregs_assert_state_consistent and account_user_time, appear at significantly deeper levels than in earlier versions. Indeed, some reach the maximum stack trace depth of the profiling tool. The results indicate a growth in both modularity and the depth of instrumentation within the kernel execution paths. Multiple performance changes become visible and interactive with Flamegraph AR. For example, time-consuming functions show up as wide frames that span over desks or walls, and deep call stacks are explored physically by approaching or gazing upward. By mapping performance traces into the spatial domain, our tool provides a compelling method for understanding systemic evolution in large-scale software like the Linux kernel.
dc.format.extent4 páginas
dc.fuente.origenSIPA
dc.identifier.doi10.1109/VISSOFT67405.2025.00024
dc.identifier.scopusidSCOPUS_ID:105018910283
dc.identifier.urihttps://doi.org/10.1109/VISSOFT67405.2025.00024
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11175675
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/107303
dc.information.autorucEscuela de Ingeniería; Sandoval Alcocer, Juan Pablo; 0000-0002-8335-4351; 1210748
dc.information.autorucEscuela de Diseño; Merino Del Campo, Leonel Alejandro; 0000-0002-5396-487X; 14305
dc.language.isoen
dc.lugar.publicacionNew Zealand
dc.nota.accesocontenido completo
dc.publisherIEEE Computer Society
dc.relation.ispartofIEEE Working Conference on Software Visualization (VISSOFT)
dc.rightsacceso abierto
dc.subjectExtended Reality
dc.subjectLinux
dc.subjectPerformance
dc.subjectSoftware Visualization
dc.titleVisualizing The Linux Kernel Performance with FlameGraph AR
dc.typecomunicación de congreso
sipa.codpersvinculados1210748
sipa.codpersvinculados14305
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