gpd-parameter-sweep

Execute systematic parameter sweeps with wave-based parallel computations and structured result aggregation.

1|Updated Mar 29, 2026

Search Tags:#data aggregation #parallel computation #parameter sweep #computational physics #scientific analysis

Ready to run

npx skills add https://github.com/CharGrnmn/roomtemp-superconductor-gpd --skill gpd-parameter-sweep-chargrnmn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gpd-parameter-sweep
Source: https://github.com/CharGrnmn/roomtemp-superconductor-gpd/tree/main/.agents/skills/gpd-parameter-sweep
Command: npx skills add https://github.com/CharGrnmn/roomtemp-superconductor-gpd --skill gpd-parameter-sweep-chargrnmn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires gpd, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of performing systematic parameter sweeps, executing parallel computations, and aggregating results, thereby streamlining the analysis of complex physical systems.

Core Features & Use Cases

Systematic Parameter Sweep: Execute a series of computations across a defined parameter space to analyze the system's behavior.
Parallel Execution: Utilize wave-based parallelism to evaluate parameter values concurrently, saving time.
Result Aggregation: Collect and structure the results into a summary report for further analysis.
Use Case: Imagine you are studying the superconducting properties of materials under varying pressure and temperature conditions. This Skill can automate the computation of these properties at each point in the parameter space and compile the results into a comprehensive report.

Quick Start

Run the 'gpd-parameter-sweep' command with the parameters and ranges you wish to test. For example: gpd-parameter-sweep --param temperature --range 0.1:10.0:20

Frequently Asked Questions about gpd-parameter-sweep

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate a parameter sweep for computational physics research?▼

To automate a parameter sweep for computational physics research, you can use the gpd-parameter-sweep command to execute computations across a defined parameter space and aggregate the results. This streamlines the analysis of complex physical systems by evaluating material properties concurrently.

What is wave-based parallelism and how does it handle concurrent parameter evaluation?▼

Wave-based parallelism is a concurrent execution mechanism that evaluates multiple parameter values simultaneously. It saves time during systematic parameter sweeps by processing waves of computations in parallel rather than sequentially across the defined parameter space.

How do I run a parameter sweep with a specific temperature range and step size?▼

To run a parameter sweep with a specific range, use the command line interface by specifying the parameter and range. For example, execute `gpd-parameter-sweep --param temperature --range 0.1:10.0:20` to evaluate the system's behavior across that defined space.

Does this parameter sweep tool require numpy and gpd to function?▼

Yes, the parameter sweep tool requires both gpd and numpy dependencies to function. These libraries provide the necessary computational framework and numerical operations required for executing parallel computations and aggregating the scientific analysis results.

Can I aggregate parallel computation results into a structured summary report?▼

Yes, you can aggregate parallel computation results into a structured summary report. The tool collects and structures the outputs from the concurrent parameter evaluations, compiling them into a comprehensive report for further scientific analysis.

What's the best way to evaluate material properties under varying pressure and temperature conditions?▼

The best way to evaluate material properties under varying conditions is to use an automated parameter sweep. It systematically executes computations across the pressure and temperature parameter space using parallel execution, then compiles the properties into a comprehensive report.