Slurmslim
What is Slurmslim
SlurmSlim is a lightweight tool designed to optimize job scheduling in Slurm by accurately estimating the required memory for scripts and programs using Model Context Protocol (MCP) and other data.
Use cases
Use cases for SlurmSlim include optimizing memory allocation for batch jobs in HPC, reducing costs in cloud computing environments, and improving job scheduling efficiency in research and development workflows.
How to use
To use SlurmSlim, clone the repository from GitHub, navigate to the SlurmSlim directory, and run the client and server scripts using the command ‘uv run client.py server.py’.
Key features
Key features of SlurmSlim include intelligent memory estimation using MCP and LLM models, cost reduction by preventing excessive memory requests, file and system awareness for precise estimations, and a lightweight design for efficiency.
Where to use
SlurmSlim is applicable in high-performance computing (HPC) environments, cloud computing, and any scenario where Slurm job scheduling is utilized and memory optimization is needed.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Slurmslim
SlurmSlim is a lightweight tool designed to optimize job scheduling in Slurm by accurately estimating the required memory for scripts and programs using Model Context Protocol (MCP) and other data.
Use cases
Use cases for SlurmSlim include optimizing memory allocation for batch jobs in HPC, reducing costs in cloud computing environments, and improving job scheduling efficiency in research and development workflows.
How to use
To use SlurmSlim, clone the repository from GitHub, navigate to the SlurmSlim directory, and run the client and server scripts using the command ‘uv run client.py server.py’.
Key features
Key features of SlurmSlim include intelligent memory estimation using MCP and LLM models, cost reduction by preventing excessive memory requests, file and system awareness for precise estimations, and a lightweight design for efficiency.
Where to use
SlurmSlim is applicable in high-performance computing (HPC) environments, cloud computing, and any scenario where Slurm job scheduling is utilized and memory optimization is needed.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
⚠️⚠️⚠️WARNING: This project will no longer be updated. If you’re interested, feel free to fork it.
SlurmSlim 💵
Optimize Slurm Job Scheduling with Intelligent Memory Estimation
Overview
SlurmSlim is a lightweight and efficient tool designed to optimize job scheduling in Slurm by accurat ely estimating the required memory for scripts and programs. By leveraging Model Context Protocol (MCP), LLM models, file sizes, and system information, SlurmSlim helps reduce computing costs by preventing over-allocated memory requests.
Features
- ✅ Intelligent Memory Estimation – Uses MCP and LLM models to predict the optimal memory allocation.
- ✅ Cost Reduction – Prevents excessive memory requests, lowering overall compute costs.
- ✅ File & System-Aware – Considers file sizes and system specs for precise estimation.
- ✅ Lightweight & Fast – Designed for efficiency with minimal overhead.
Installation
git clone https://github.com/JianYang-Lab/SlurmSlim.git
cd SlurmSlim
uv sync # If applicable
Usage
uv run client.py server.py
Example Output
Estimated Memory: 8.2 GB Suggested Slurm Command: sbatch --mem=8500M job_script.sh
Why Use SlurmSlim?
- 🔹 Saves Money – No more over-provisioning, reducing unnecessary cloud or HPC costs.
- 🔹 Improves Efficiency – Ensures jobs run smoothly without excessive memory requests.
- 🔹 Seamless Integration – Works directly with Slurm job scripts and scheduling workflows.
Future Work
- Extend support for CPU & GPU resource optimization
- Integration with other job schedulers (e.g., PBS, LSF)
- Advanced machine learning models for prediction
License
📜 MIT License
Contributors
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
