Code Profiler
A developer-focused tool to profile code performance, reveal hotspots, and optimize runtime efficiency with multi-language support.
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Om dette verktøyet
Core Logic & Features: It supports instrumentation and sampling strategies to collect CPU time, wall-clock time, and allocation data. Outputs include call graphs, flame graphs, and memory charts. Core features: multi-language support (e.g., Node.js, Python, Java, Go, C/C++), per-function hotspots, cross-process tracing, and customizable profiling windows. Optional features: heap snapshots, thread activity, and CI-friendly exports.
Inputs & Outputs: Inputs include language, project path or code snippet, profiling mode, duration, sampling rate, and environment flags. Outputs are structured profiles (JSON), call trees, flame-graph data, and summary CSVs or SVGs. The tool ensures deterministic formatting for dashboards and AI agents.
Algorithms & Calculations: Uses instrumentation to obtain precise enter/exit timestamps or sampling to approximate CPU usage; merges stacks into unified call graphs; aggregates allocations by type; estimates profiling overhead; applies filters to prune noise.
Error & Edge Cases: Handles unsupported languages, missing inputs, very short durations, or high-overhead workloads by warning or reducing data collection; external libraries and dynamic code paths may require special handling; results may be conservative if data is partial.
Industry/Region & Localization: Time units in seconds, milliseconds, nanoseconds; memory in bytes, KB, MB, GB. Works across Linux, Windows, macOS; aligns with standard profiling conventions and CI tooling. Localization isn't required for numeric outputs.
Assumptions & Exclusions: Assumes access to source or binaries; excludes UI profiling, frontend code, or non-executable assets; not an APM platform; not real-time production profiling without safety controls.
Hvordan bruke
- Provide inputs: target language, project path or code snippet, profiling mode, and duration.
- Configure environment: set sampling rate or instrumentation level.
- Run profiling: start the profiler and execute workload.
- Review outputs: examine flame graph, call tree, and memory charts.
- Act on results: identify hotspots and optimize.
Frequently Asked Questions
Finn raske svar
Which languages are supported?
How should I interpret flame graphs?
What is profiling overhead?
Can results be integrated into CI?
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