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3 dépôts

Awesome GitHub RepositoriesDifferential Profilers

Tools for calculating and visualizing the delta between two performance datasets to highlight regressions.

Distinct from Performance Profiling: Distinct from Performance Profiling: focuses specifically on comparative analysis between two datasets rather than general resource monitoring.

Explore 3 awesome GitHub repositories matching testing & quality assurance · Differential Profilers. Refine with filters or upvote what's useful.

Awesome Differential Profilers GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • brendangregg/flamegraphAvatar de brendangregg

    brendangregg/FlameGraph

    19,307Voir sur GitHub↗

    FlameGraph is a performance profiling and visualization toolkit designed to identify bottlenecks in software execution. It functions as a processing engine that transforms raw stack trace samples into interactive, hierarchical diagrams. By representing aggregated execution frequency as nested rectangles, the tool allows developers to visualize hot code paths and analyze system behavior across both kernel and user-space environments. The project distinguishes itself through its ability to perform differential profile analysis, which highlights performance regressions or improvements by compari

    Performs differential profile analysis to highlight performance regressions or improvements by comparing two datasets side-by-side.

    Perl
    Voir sur GitHub↗19,307
  • xanderfrangos/twinkle-trayAvatar de xanderfrangos

    xanderfrangos/twinkle-tray

    7,961Voir sur GitHub↗

    Twinkle Tray is a Windows display manager and DDC/CI monitor control utility designed to manage hardware settings across internal and external displays. It functions as a system tray application that allows for the adjustment of brightness, contrast, and volume on multiple monitors. The project provides multi-display brightness normalization to balance backlight levels across different hardware profiles for a consistent visual experience. It also includes an automatic brightness scheduler that modifies luminosity based on the time of day or system idle states. Beyond the user interface, the

    Calculates relative brightness offsets across different monitors to achieve a consistent visual experience.

    JavaScriptbacklightbacklight-brightnessbrightness
    Voir sur GitHub↗7,961
  • jrfonseca/gprof2dotAvatar de jrfonseca

    jrfonseca/gprof2dot

    3,434Voir sur GitHub↗

    gprof2dot is a performance graph generator and visualizer that converts gprof GNU profiler execution profiles into Graphviz DOT files. It transforms raw profiler data into a directed graph to map function call hierarchies and identify software bottlenecks. The tool employs heuristic-based color mapping to highlight performance hotspots by assigning colors to nodes and edges based on execution time percentages. It also supports differential profile analysis, allowing for the comparison of two distinct execution graphs to identify changes in timing and call counts between runs. To improve visu

    Provides capabilities to compare two execution graphs to calculate and visualize changes in performance metrics between runs.

    Pythonc-plus-plusgraphprofiling
    Voir sur GitHub↗3,434
  1. Home
  2. Testing & Quality Assurance
  3. Performance Testing and Analysis
  4. Performance Profiling
  5. Differential Profilers

Explorer les sous-tags

  • Differential Visualization InvertersUtilities that reverse the color-coding logic of differential profiles to highlight code paths that have decreased in frequency. **Distinct from Differential Profilers:** Distinct from Differential Profilers: focuses specifically on the visual inversion of comparative data rather than the calculation of the delta itself.
  • Profile Normalizers1 sous-tagTools for adjusting and scaling stack trace counts between datasets to enable accurate comparative analysis. **Distinct from Differential Profilers:** Distinct from Differential Profilers: focuses on the data normalization step required for accurate comparison rather than the generation of the differential view itself.