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nvdv avatar

nvdv/vprof

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Vprof

vprof is a visual profiling tool for Python designed to identify execution bottlenecks and monitor memory consumption. It functions as a CPU and memory profiler that transforms performance data into interactive visualizations to analyze processor time and call stacks.

The project distinguishes itself through a suite of visual diagnostics, including flame graphs for stack visualization and heatmaps that map execution frequency and duration directly onto source code. It also includes a remote performance monitor capable of capturing function-specific metrics from a running server and streaming that data to a separate visualization tool.

The tool covers broad capability areas including sampling-based CPU profiling, line-level memory monitoring through garbage collector tracking, and profile data persistence for offline analysis. These utilities allow for the auditing of source code efficiency and the identification of memory leaks.

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Features

  • Python Profilers - Provides a comprehensive suite for measuring execution time and identifying CPU bottlenecks in Python programs.
  • Code Execution Profilers - Analyzes runtime performance of code using heatmaps that show execution counts and run times for every line.
  • Source Code Heatmaps - Produces heatmaps of source code showing execution counts and run times for every line.
  • CPU Profilers - Captures periodic snapshots of the call stack to estimate execution time and identify performance bottlenecks.
  • Visual Profiling Toolkits - Provides an interactive visual interface for analyzing Python performance via flame graphs, heatmaps, and statistics.
  • Execution Heatmaps - Overlays execution frequency and duration data directly onto source code for visual performance analysis.
  • Execution Tracking - Instruments source code to record execution counts and timing statistics for every individual line of code.
  • Memory Leak Detectors - Interfaces with the Python memory manager to track object allocations and identify memory leaks.
  • Memory Consumption Trackers - Tracks objects in the garbage collector and monitors process memory usage to identify leaks and heavy allocations.
  • Python Execution Visualizations - Generates interactive charts and heatmaps to analyze how a Python program performs across its call stack.
  • Line-Level Resource Monitors - Provides granular profiling that pinpoints specific lines of code responsible for memory consumption and performance overhead.
  • Flame Graphs - Generates interactive flame graphs of call stacks to identify resource-intensive functions and processing bottlenecks.
  • Performance Code Auditing - Visualizes line-by-line execution counts and run times through heatmaps to optimize specific blocks of code.
  • Remote Profiling Streams - Transports profiling metrics from a target Python process to a separate visualization server via network sockets.
  • Remote Profiling Interfaces - Implements interfaces to capture and render performance statistics from functions running on a remote server.
  • Remote Function Profiling - Deno profiling utility that captures performance metrics for individual functions by routing data to a remote visualization server.
  • Remote Monitoring - Captures and renders performance statistics from functions running on a remote server.
  • Program Execution Visualization - Transforms raw profiler data into interactive charts and visual maps of program resource consumption.
3,979 stars·151 forks·Python·BSD-2-Clause·25 views

Star history

Star history chart for nvdv/vprofStar history chart for nvdv/vprof

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does nvdv/vprof do?

vprof is a visual profiling tool for Python designed to identify execution bottlenecks and monitor memory consumption. It functions as a CPU and memory profiler that transforms performance data into interactive visualizations to analyze processor time and call stacks.

What are the main features of nvdv/vprof?

The main features of nvdv/vprof are: Python Profilers, Code Execution Profilers, Source Code Heatmaps, CPU Profilers, Visual Profiling Toolkits, Execution Heatmaps, Execution Tracking, Memory Leak Detectors.

Which projects share features with nvdv/vprof?

Projects with overlapping indexed features include: plasma-umass/scalene — Scalene is a high-performance diagnostic utility designed to measure resource consumption during the execution of… uber-archive/go-torch — go-torch is a profiling tool for capturing the execution state of Go programs and transforming raw binary data into… pythonprofilers/memory_profiler — This project is a diagnostic utility for monitoring and analyzing memory consumption in Python applications. It… joerick/pyinstrument — pyinstrument is a statistical sampling profiler for Python that records the call stack at regular intervals to… kdab/hotspot — Hotspot is a graphical user interface for analyzing and visualizing performance data captured by the Linux perf tool.… google/orbit — Orbit is a set of specialized tools for C and C++ performance profiling, binary symbol mapping, and remote process and…

Projects sharing features with Vprof

These projects share indexed features with Vprof. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • plasma-umass/scaleneplasma-umass avatar

    plasma-umass/scalene

    13,449View on GitHub↗

    Scalene is a high-performance diagnostic utility designed to measure resource consumption during the execution of Python applications. It functions as a line-level monitor, providing granular insights that pinpoint the specific source code responsible for performance overhead. The tool distinguishes itself through statistical profiling that captures stack traces and resource usage without requiring manual instrumentation of the source code. It tracks CPU, GPU, and memory consumption by intercepting library-level calls and hardware driver commands, allowing for the analysis of both managed and

    Pythoncpucpu-profilinggpu
    View on GitHub↗13,449
  • uber-archive/go-torchuber-archive avatar

    uber-archive/go-torch

    3,941View on GitHub↗

    go-torch is a profiling tool for capturing the execution state of Go programs and transforming raw binary data into visual representations of program performance. It functions as a flame graph profiler and performance visualization utility that identifies expensive code paths through the collection of CPU and memory stack traces. The tool features a network-capable remote process profiler that connects to endpoints to capture and export execution profiles from Go binaries. It utilizes stochastic profiling to synthesize execution data into call graphs, allowing for the identification of bottle

    Go
    View on GitHub↗3,941
  • pythonprofilers/memory_profilerpythonprofilers avatar

    pythonprofilers/memory_profiler

    4,571View on GitHub↗

    This project is a diagnostic utility for monitoring and analyzing memory consumption in Python applications. It provides tools for tracking resource usage at the process level and performing detailed, line-by-line analysis to identify memory leaks and performance bottlenecks. The tool distinguishes itself through its ability to aggregate memory metrics across entire process trees, capturing the total resource impact of both parent and child processes. It supports time-series visualization of memory usage over the duration of a script, allowing for the identification of long-term consumption p

    Python
    View on GitHub↗4,571
  • joerick/pyinstrumentjoerick avatar

    joerick/pyinstrument

    7,638View on GitHub↗

    pyinstrument is a statistical sampling profiler for Python that records the call stack at regular intervals to identify performance bottlenecks with low overhead. It tracks wall-clock time, including I/O and external service calls, and provides specialized profiling for asynchronous programs by attributing time spent awaiting tasks to the calling function. The project converts captured execution data into interactive HTML reports, JSON, and flamecharts. It includes a call stack visualizer to simplify the analysis of execution paths and supports the profiling of individual cells within interac

    Pythonasyncdjangoperformance
    View on GitHub↗7,638
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